Pluralsight Path. Data Science with Microsoft Azure (2021)

Creation Time	May 16, 2024, 2:10 a.m.
Last Access Time	Nov. 16, 2024, 7:12 a.m.
File Size	3.2 GB
Keywords	Pluralsight Data 2021 Science Azure Path with Microsoft
Total Requests	44
Total Files	1000
Magnet Link	Please wait ...
	Download (Magnet Link) Play (Watch Online)

F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/3. Working with Azure Databricks/5. Demo - Azure Databricks with Azure Data Lake Storage Gen2.mp4 60.5 MB
F3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/3. Using Continuous Integration and Continuous Deployment/4. Creating Pipelines.mp4 48.8 MB
E5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/exercise.7z 43.8 MB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/4. Using the Kusto Query Language (KQL)/6. Demo - Working with KQL - Timeseries.mp4 42.0 MB
F1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/3. Import and Prepare Data for Modeling/2. Data Preprocessing with Microsoft AzureML.mp4 37.6 MB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/4. Using the Kusto Query Language (KQL)/5. Demo - Working with KQL - Basic.mp4 37.2 MB
F3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/2. Deploying a Machine Learning Model/5. Creating and Deploying.mp4 37.0 MB
E5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/3. Utilizing the SIFT and HOG Algorithms for Feature Detection/4. Extracting and Matching Features with SIFT.mp4 35.4 MB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/5. Data Ingestion for ADX/6. Demo - Data Ingestion Using EventHubs and .Net Custom Code.mp4 34.8 MB
B2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/3. Extracting and Loading Data into an Azure Workflow/6. HD Insights Demo.mp4 32.8 MB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/4. Model Evaluation and Summarizing Results/6. Demo - Create Model and Perform Predictive Analytics Part3.mp4 32.6 MB
F1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/5. Tuning Hyperparameters and AutoML/3. Automated Machine Learning Experiment Using Python SDK.mp4 31.8 MB
F1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/3. Import and Prepare Data for Modeling/1. Creating and Registering Microsoft AzureML Datastore.mp4 29.9 MB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/5. Reviewing Results with Stakeholders/4. Demo - Communicating Insights using MatPlotLib.mp4 28.5 MB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/3. Working with Azure Databricks/4. Demo - Configuring and Working with Azure Databricks.mp4 27.8 MB
E5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/3. Utilizing the SIFT and HOG Algorithms for Feature Detection/6. Extracting and Matching Features with HOG.mp4 27.2 MB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/3. Working with Azure Databricks/6. Demo - Performing Exploratory Data Analysis using Azure Databricks.mp4 25.8 MB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/4. Model Evaluation and Summarizing Results/5. Demo - Create Model and Perform Predictive Analytics Part 2.mp4 24.5 MB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/3. Working with Azure Databricks/7. Demo - Working with Streaming Data Using Azure Databricks and Event Hubs.mp4 23.6 MB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/5. Reviewing Results with Stakeholders/3. Demo - Communicating Insights using Power BI.mp4 23.4 MB
E5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/4. Leveraging Convolutional Neural Networks for Feature Extraction/5. Creating a CNN for Classification.mp4 23.3 MB
D1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/2. Determining Which Tools to Use/4. Tools.mp4 23.1 MB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/6. Managing Azure Data Explorer/5. Demo - Managing ADX Database Permissions.mp4 23.1 MB
F1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/4. Training, Tracking, and Monitoring a Model/4. Training Script and Estimators in AzureML.mp4 22.3 MB
F1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/5. Tuning Hyperparameters and AutoML/2. Hyperparameter Tuning - Demo.mp4 21.2 MB
D1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/4. Using the New Development Environment/3. Iris Demo.mp4 21.1 MB
B2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/3. Extracting and Loading Data into an Azure Workflow/4. Azure Data Factory Demo.mp4 21.0 MB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/5. Data Ingestion for ADX/7. Demo - Data Ingestion Using EventGrids and Blob Storage.mp4 20.8 MB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/4. Using the Kusto Query Language (KQL)/8. Demo - Data Sharing and Visualization Using Power BI.mp4 20.0 MB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/3. Building the ADX Environment/4. Demo - Create ADX Cluster Using PowerShell.mp4 19.8 MB
E5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/4. Leveraging Convolutional Neural Networks for Feature Extraction/4. Working with MNIST.mp4 19.5 MB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/4. Using Principal Component Analysis to Reduce Numeric Feature Sets/3. How PCA Works.mp4 19.3 MB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/4. Using the Kusto Query Language (KQL)/7. Demo - Data Obfuscation in KQL.mp4 18.7 MB
F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/2. Evaluating Model Effectiveness/4. Demo - Inspecting an Azure ML Pipeline.mp4 18.3 MB
F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/3. Improving Model Performance/4. Demo - Creating an Automated ML Experiment.mp4 17.8 MB
F1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/2. Understanding Azure Machine Learning service/2. Creating and Deleting Workspace.mp4 17.8 MB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/2. Understanding How Feature Set Complexity Impacts Model Quality/6. Demo.mp4 17.6 MB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/15. Demo - Word Embeddings with BERT on AMLS.mp4 17.4 MB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/3. Performing Feature Extraction/4. Performing Feature Extraction on Unstructured Text.mp4 17.3 MB
B3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Transforming Data into Usable Datasets/3. R Demo.mp4 17.2 MB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/6. Managing Azure Data Explorer/7. Demo - Scaling the ADX Cluster.mp4 17.0 MB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/6. Managing Azure Data Explorer/6. Demo - ADX Health and Performance Monitoring.mp4 16.7 MB
F1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/2. Understanding Azure Machine Learning service/3. Setting up Environments.mp4 16.7 MB
E5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/3. Utilizing the SIFT and HOG Algorithms for Feature Detection/5. HOG Introduction.mp4 16.6 MB
F1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/5. Tuning Hyperparameters and AutoML/4. Automated Machine Learning Experiment Using Visual Interface.mp4 16.4 MB
F1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/4. Training, Tracking, and Monitoring a Model/2. Metrics Logging in AzureML.mp4 16.3 MB
F1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/4. Training, Tracking, and Monitoring a Model/1. Setting up Compute Target.mp4 15.8 MB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/4. Model Evaluation and Summarizing Results/4. Demo - Create Model and Perform Predictive Analytics Part 1.mp4 15.7 MB
B3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Transforming Data into Usable Datasets/4. Python Demo.mp4 15.5 MB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/3. Performing Feature Extraction/2. Performing Feature Extraction.mp4 15.1 MB
B3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Transforming Data into Usable Datasets/2. Data Structures.mp4 14.7 MB
A2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/3. Determining Data Availability/8. Azure Availability Features.mp4 14.6 MB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/3. Performing Feature Extraction/5. Demo - Human Face or Not Human Face.mp4 14.5 MB
F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/4. Assessing Model Explainability/5. Demo - Touring the Azure Python Interpretability SDK.mp4 14.4 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/4. Preparing Input Data for Machine Learning Models/03. Demo - Exploratory Data Analysis.mp4 14.4 MB
B2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Identifying Potential Data Sources/2. Data.mp4 13.9 MB
E5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/3. Utilizing the SIFT and HOG Algorithms for Feature Detection/3. SIFT Introduction.mp4 13.8 MB
D1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/4. Using the New Development Environment/5. Azure Backup Services Demo.mp4 13.5 MB
F1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/3. Import and Prepare Data for Modeling/3. Microsoft AzureML Datasets.mp4 13.4 MB
A2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/4. Assessing Ethical and Legal Data Compliance/3. Demo - Azure Security, Privacy, and Compliance.mp4 13.2 MB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/2. Exploring Your Dataset for Feature Selection and Extraction/5. Dataset Exploration Demo.mp4 13.1 MB
E5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/2. Exploring Computer Vision on Azure/5. Launching a Notebook Instance.mp4 12.9 MB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/3. Applying Criteria-based Feature Reduction Techniques/7. Demo.mp4 12.8 MB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/5. Performing Feature Selection/6. Compute Linear Correlation Demo.mp4 12.7 MB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/5. Performing Feature Selection/4. Fisher Linear Discriminant Analysis Demo.mp4 12.7 MB
F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/2. Evaluating Model Effectiveness/5. Demo - Scoring and Evaluating the Pipeline Model.mp4 12.5 MB
E5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/2. Exploring Computer Vision on Azure/2. Introduction to Computer Vision.mp4 12.4 MB
F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/4. Assessing Model Explainability/4. Setting Up an Experiment in a Jupyter Notebook.mp4 12.3 MB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/3. Building the ADX Environment/3. Create ADX Cluster Using PowerShell.mp4 12.2 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/6. Role of Feature Engineering in Machine Learning/7. Demo - One-hot Encoding Categorical Variables.mp4 12.2 MB
F1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/5. Tuning Hyperparameters and AutoML/1. Hyperparameter Tuning - Theory.mp4 12.2 MB
D1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/2. Determining Which Tools to Use/2. Introduction to Data Science.mp4 12.1 MB
E5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/4. Leveraging Convolutional Neural Networks for Feature Extraction/3. Convolutional Neural Network Overview.mp4 11.9 MB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/3. Approaching Normalization and Standardization/4. Demo - Outlier Detection in Python.mp4 11.9 MB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/11. Demo - The Hashing Trick.mp4 11.4 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/7. Split a Data Set into Training and Testing Subsets/4. Splitting Data for Model Tuning.mp4 11.4 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/2. Getting Started with Azure Machine Learning/6. Demo - Creating an Azure Machine Learning Studio Workspace.mp4 11.3 MB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/exercise.7z 11.2 MB
F1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/4. Training, Tracking, and Monitoring a Model/5. Distributed Training in AzureML.mp4 11.2 MB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/3. Building the ADX Environment/6. Demo - Create ADX Cluster Using Command Line Interface.mp4 11.2 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/7. Split a Data Set into Training and Testing Subsets/5. Demo - Cross-validation.mp4 11.0 MB
D1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/3. Setting up a Development Environment/2. Provisioning an Environment.mp4 10.8 MB
B3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Transforming Data into Usable Datasets/7. Bad Data.mp4 10.7 MB
B2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Identifying Potential Data Sources/5. Azure Data Catalog Demo.mp4 10.6 MB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/3. Building the ADX Environment/2. Demo - Create ADX Cluster Using Azure Portal.mp4 10.6 MB
F1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/2. Understanding Azure Machine Learning service/1. Overview of Microsoft Azure Machine Learning service.mp4 10.5 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/7. Split a Data Set into Training and Testing Subsets/2. Demo - Training and Testing on Same Data.mp4 10.4 MB
E5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/3. Utilizing the SIFT and HOG Algorithms for Feature Detection/2. Image Processing Techniques.mp4 10.3 MB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/12. Demo - Frequency Filtering.mp4 10.3 MB
D1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/3. Setting up a Development Environment/3. Creating a DSVM.mp4 10.1 MB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/10. Demo - Stopword Removal.mp4 10.0 MB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/2. Exploring Your Dataset for Feature Selection and Extraction/2. What Is a Feature in Machine Learning.mp4 9.8 MB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/13. Demo - Locality-sensitive Hashing.mp4 9.6 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/6. Role of Feature Engineering in Machine Learning/8. Demo - Learning with Counts Categorical Variables.mp4 9.6 MB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/09. Demo - Word Embeddings Using Word2Vec.mp4 9.5 MB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/3. Working with Azure Databricks/3. Understanding Apache Spark and Notebook.mp4 9.5 MB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/4. Performing Feature Normalization/7. Encoding Features Demo.mp4 9.5 MB
B2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Identifying Potential Data Sources/7. Azure Open Datasets Demo.mp4 9.5 MB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/2. Setting the Stage/08. Gaussian Distributions.mp4 9.5 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/4. Preparing Input Data for Machine Learning Models/07. Demo - Data Transformation.mp4 9.5 MB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/03. Demo - Configure AMLS.mp4 9.4 MB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/4. Using Principal Component Analysis to Reduce Numeric Feature Sets/6. Demo.mp4 9.4 MB
E5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/4. Leveraging Convolutional Neural Networks for Feature Extraction/2. Neural Network Overview.mp4 9.4 MB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/4. Performing Feature Normalization/5. Normalize Data Demo.mp4 9.4 MB
A2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/2. Authentication and Authorization Methods/5. Demo - Working with Tokens.mp4 9.3 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/2. Getting Started with Azure Machine Learning/8. Demo - Exploring the Dataset.mp4 9.2 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/7. Split a Data Set into Training and Testing Subsets/6. Demo - Model Selection.mp4 9.1 MB
B2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Identifying Potential Data Sources/4. Azure Data Catalog.mp4 9.1 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/3. Differentiating Data, Features, Targets, and Models/7. Demo - Modifying the Metadata of Datasets.mp4 9.0 MB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/5. Processing Categorical or Text Feature Sets/3. LDA.mp4 9.0 MB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/4. Performing Feature Normalization/3. Clip Values Demo.mp4 8.9 MB
A2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/2. Authentication and Authorization Methods/4. Access Keys and SAS.mp4 8.9 MB
F1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/4. Training, Tracking, and Monitoring a Model/3. Setting up Run Object.mp4 8.9 MB
B2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/exercise.7z 8.9 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/5. Handling Missing Data/03. Demo - Listwise Deletion.mp4 8.8 MB
A2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/3. Determining Data Availability/3. Azure SQL High Availability.mp4 8.8 MB
D1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/2. Determining Which Tools to Use/3. Identifying Constraints.mp4 8.7 MB
B3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Transforming Data into Usable Datasets/5. Large Data Sets.mp4 8.7 MB
E5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/2. Exploring Computer Vision on Azure/3. Approaches to Computer Vision.mp4 8.6 MB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/07. Demo - NLTK Tokenizers.mp4 8.5 MB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/5. Processing Categorical or Text Feature Sets/6. Demo.mp4 8.5 MB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/6. Going beyond PCA to Reduce Complexity in Numeric Feature Sets/6. Demo.mp4 8.5 MB
B3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Transforming Data into Usable Datasets/6. SQL Data Sampling.mp4 8.4 MB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/2. Setting the Stage/6. Microsofts Team Data Science Process.mp4 8.4 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/3. Differentiating Data, Features, Targets, and Models/3. 6 Characteristics of a Good Feature.mp4 8.4 MB
F3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/4. Managing a Models Lifecycle/4. Application Insights.mp4 8.3 MB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/5. Performing Feature Selection/5. Permutation Feature Importance Demo.mp4 8.3 MB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/03. One-hot and Count Vector Encoding.mp4 8.2 MB
B2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/3. Extracting and Loading Data into an Azure Workflow/2. Extracting and Loading.mp4 8.2 MB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/2. Setting the Stage/2. Data Science Overview.mp4 8.2 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/7. Split a Data Set into Training and Testing Subsets/3. Demo - Split Data into Training and Test Set.mp4 8.2 MB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/2. Understanding How Feature Set Complexity Impacts Model Quality/1. Introduction and Module Overview.mp4 7.9 MB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/07. Demo - TF-IDF Encoding.mp4 7.9 MB
A2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/2. Authentication and Authorization Methods/2. Authentication and Authorization on Azure.mp4 7.8 MB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/06. Demo - Sentence and Word Tokenization.mp4 7.8 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/4. Preparing Input Data for Machine Learning Models/10. Demo - Discretizing Data.mp4 7.8 MB
E5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/2. Exploring Computer Vision on Azure/4. Azure Services for Computer Vision.mp4 7.7 MB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/4. Performing Feature Normalization/6. Principal Component Analysis Demo.mp4 7.7 MB
B2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/3. Extracting and Loading Data into an Azure Workflow/3. Azure Data Factory.mp4 7.7 MB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/3. Approaching Normalization and Standardization/5. Demo - Imputation in Python.mp4 7.6 MB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/4. Performing Feature Normalization/4. Group Data into Bins Demo.mp4 7.6 MB
B2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Identifying Potential Data Sources/3. Discovering Data.mp4 7.5 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/4. Preparing Input Data for Machine Learning Models/04. Demo - Data Cleaning (Erroneous Data).mp4 7.4 MB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/2. Understanding How Feature Set Complexity Impacts Model Quality/5. How Feature Set Complexity Impacts Model Interpretability.mp4 7.4 MB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/3. Applying Criteria-based Feature Reduction Techniques/5. Bivariate Techniques.mp4 7.4 MB
D1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/4. Using the New Development Environment/2. Running a Test Experiment.mp4 7.4 MB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/5. Processing Categorical or Text Feature Sets/2. How Can You Process Categorical or Text Feature Sets.mp4 7.3 MB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/3. Quantifying the Business Problem/05. Defining Business Metrics.mp4 7.3 MB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/3. Working with Azure Databricks/2. Understanding the Azure Databricks Ecosystem.mp4 7.3 MB
F3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/2. Deploying a Machine Learning Model/3. Creating Azure Machine Learning.mp4 7.2 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/2. Getting Started with Azure Machine Learning/7. Demo - Creating an Azure Machine Learning Service Workspace.mp4 7.2 MB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/4. Model Evaluation and Summarizing Results/2. Model Training Process.mp4 7.1 MB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/exercise.7z 7.1 MB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/20. Demo - N-grams.mp4 7.0 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/4. Preparing Input Data for Machine Learning Models/05. Demo - Data Cleaning (Outliers).mp4 7.0 MB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/04. Demo - Bag-of-words.mp4 7.0 MB
B3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/3. Wrangling Data/2. Aggregation.mp4 6.9 MB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/5. Performing Feature Selection/3. Fliter Based Feature Selection Demo.mp4 6.9 MB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/2. Azure Data Explorer (ADX) Overview/6. Azure Data Explorer Capabilities.mp4 6.8 MB
B3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/exercise.7z 6.8 MB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/2. Azure Data Explorer (ADX) Overview/3. Data Exploration and Visualization.mp4 6.8 MB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/3. Quantifying the Business Problem/10. Quantifying the Risks for the Data Science Project.mp4 6.8 MB
B3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/3. Wrangling Data/3. Azure Notebooks Demo.mp4 6.8 MB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/2. Azure Data Explorer (ADX) Overview/4. Data Exploration in Azure (ADX).mp4 6.7 MB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/exercise.7z 6.6 MB
B3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Transforming Data into Usable Datasets/8. Handling Bad Data.mp4 6.6 MB
A2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/4. Assessing Ethical and Legal Data Compliance/1. Ethical and Legal Compliance.mp4 6.6 MB
D1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/4. Using the New Development Environment/4. Saving Work.mp4 6.5 MB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/3. Applying Criteria-based Feature Reduction Techniques/4. How Statistical Tests Work.mp4 6.4 MB
F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/2. Evaluating Model Effectiveness/3. Scoring and Evaluating an Azure ML Pipeline.mp4 6.3 MB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/2. Exploring Your Dataset for Feature Selection and Extraction/3. Exploring Your Data and Identifying the Distribution of Your Da.mp4 6.3 MB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/6. Going beyond PCA to Reduce Complexity in Numeric Feature Sets/3. How k-means Works.mp4 6.3 MB
B2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/3. Extracting and Loading Data into an Azure Workflow/5. HD Insights.mp4 6.3 MB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/4. Defining Normalization and Standardization Techniques/03. Demo - Common Scaling Approaches.mp4 6.2 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/5. Handling Missing Data/05. Demo - Using Indicator Variables.mp4 6.2 MB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/3. Applying Criteria-based Feature Reduction Techniques/3. Univariate Techniques.mp4 6.1 MB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/2. Setting the Stage/09. Calculating the Mean, Median, and Mode.mp4 6.1 MB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/02. Encoding Text as Numbers.mp4 6.1 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/5. Handling Missing Data/08. Demo - Replace with MICE.mp4 6.0 MB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/14. BERT.mp4 6.0 MB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/10. Feature Hashing.mp4 6.0 MB
B3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/3. Wrangling Data/4. Data Labeling.mp4 6.0 MB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/2. Azure Data Explorer (ADX) Overview/2. Data Science Overview.mp4 5.8 MB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/2. Setting the Stage/3. Understanding the Modeling Process.mp4 5.6 MB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/6. Going beyond PCA to Reduce Complexity in Numeric Feature Sets/4. Autoencoders.mp4 5.6 MB
A2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/3. Determining Data Availability/1. Data Availability Concepts.mp4 5.6 MB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/4. Using Principal Component Analysis to Reduce Numeric Feature Sets/4. KPCA.mp4 5.5 MB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/5. Data Ingestion for ADX/2. KQL Schema Mapping for Data Ingestion.mp4 5.5 MB
A2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/3. Determining Data Availability/5. Cosmos DB Availability.mp4 5.5 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/8. Identify Data-level Issues In Machine Learning Models/3. Demo - SMOTE.mp4 5.4 MB
A2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/2. Authentication and Authorization Methods/1. The Shared Responsibility Model.mp4 5.4 MB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/4. Recommending Next Steps Based on Available Data/3. Synthetic Training Data.mp4 5.3 MB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/05. Tokenization and Cleaning.mp4 5.3 MB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/3. Building the ADX Environment/5. Create ADX Cluster Using Command Line Interface.mp4 5.3 MB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/5. Processing Categorical or Text Feature Sets/4. Dictionary Learning.mp4 5.2 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/4. Preparing Input Data for Machine Learning Models/08. Demo - Reducing Data (Record Sampling).mp4 5.2 MB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/14. Demo - Stemming.mp4 5.1 MB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/4. Performing Feature Normalization/1. Performing Feature Normalization.mp4 5.0 MB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/4. Using Principal Component Analysis to Reduce Numeric Feature Sets/2. PCA.mp4 4.9 MB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/04. Identifying the Hard-skills.mp4 4.9 MB
F3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/3. Using Continuous Integration and Continuous Deployment/2. Tracking Models.mp4 4.9 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/3. Differentiating Data, Features, Targets, and Models/4. Define Target for ML Problems.mp4 4.8 MB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/3. Applying Criteria-based Feature Reduction Techniques/1. Introduction and Module Overview.mp4 4.8 MB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/4. Using Principal Component Analysis to Reduce Numeric Feature Sets/1. Introduction and Module Overview.mp4 4.8 MB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/2. Setting the Stage/4. Model Training.mp4 4.7 MB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/3. Applying Criteria-based Feature Reduction Techniques/6. Multivariate Techniques.mp4 4.7 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/2. Getting Started with Azure Machine Learning/2. What Is Machine Learning.mp4 4.7 MB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/12. Locality-sensitive Hashing.mp4 4.7 MB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/2. Azure Data Explorer (ADX) Overview/1. Module Overview.mp4 4.6 MB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/3. Quantifying the Business Problem/07. Managing Technical Metrics.mp4 4.6 MB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/1. Course Overview/1. Course Overview.mp4 4.6 MB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/2. Setting the Stage/5. Model Evaluation.mp4 4.5 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/4. Preparing Input Data for Machine Learning Models/06. Demo - Data Cleaning (Duplicate Rows).mp4 4.5 MB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/4. Using the Kusto Query Language (KQL)/2. Understanding the Kusto Query Language (KQL).mp4 4.5 MB
D1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/3. Setting up a Development Environment/5. Environment Management.mp4 4.5 MB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/2. Understanding How Feature Set Complexity Impacts Model Quality/3. Sources of Model Error.mp4 4.5 MB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/08. Word Embeddings.mp4 4.5 MB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/02. Prerequisites.mp4 4.4 MB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/05. Demo - Bag-of-n-grams.mp4 4.4 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/3. Differentiating Data, Features, Targets, and Models/5. Demo - Exploring Datasets for Different Problems.mp4 4.3 MB
F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/2. Evaluating Model Effectiveness/2. Preliminary Terminology.mp4 4.3 MB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/exercise.7z 4.3 MB
A2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/3. Determining Data Availability/2. Availability on Blob Storage.mp4 4.3 MB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/4. Model Evaluation and Summarizing Results/3. Model Training Techniques.mp4 4.3 MB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/4. Using Principal Component Analysis to Reduce Numeric Feature Sets/5. PCA Limitations.mp4 4.3 MB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/5. Leveraging Nominal Data in Machine Learning/7. Demo - Label Encoding and XGBoost.mp4 4.3 MB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/18. Demo - Lemmatization.mp4 4.3 MB
D1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/3. Setting up a Development Environment/4. Version Management.mp4 4.2 MB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/5. Leveraging Nominal Data in Machine Learning/3. Data Measurement Scales.mp4 4.2 MB
F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/4. Assessing Model Explainability/3. Unintended Bias and Interpretability.mp4 4.2 MB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/3. Approaching Normalization and Standardization/2. Machine Learning Process Distilled.mp4 4.1 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/6. Role of Feature Engineering in Machine Learning/2. Why Feature Engineering.mp4 4.1 MB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/6. Going beyond PCA to Reduce Complexity in Numeric Feature Sets/5. Manifold Learning.mp4 4.1 MB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/4. Defining Normalization and Standardization Techniques/09. Demo - Z-score.mp4 4.1 MB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/01. What Is Data Science.mp4 4.1 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/exercise.7z 4.1 MB
F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/3. Improving Model Performance/5. Demo - Interpreting the Experiment Results.mp4 4.0 MB
F3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/2. Deploying a Machine Learning Model/2. Azure Machine Learning.mp4 4.0 MB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/6. Going beyond PCA to Reduce Complexity in Numeric Feature Sets/2. k-means Model Stacking.mp4 4.0 MB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/3. Quantifying the Business Problem/06. Business Metrics Classifications.mp4 4.0 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/8. Identify Data-level Issues In Machine Learning Models/7. Problem with High-dimensional Datasets.mp4 4.0 MB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/4. Defining Normalization and Standardization Techniques/07. Demo - Correcting Heteroscedasticity.mp4 4.0 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/2. Getting Started with Azure Machine Learning/1. Introduction.mp4 3.9 MB
F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/3. Improving Model Performance/2. Detecting and Preventing Overfitting.mp4 3.9 MB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/2. Understanding How Feature Set Complexity Impacts Model Quality/4. Measuring and Detecting Problems Due to Feature Set Complexity.mp4 3.9 MB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/07. Selecting the Right Stakeholders.mp4 3.9 MB
F3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/3. Using Continuous Integration and Continuous Deployment/3. Continuous Deployment.mp4 3.9 MB
A2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/3. Determining Data Availability/7. High Quality Datasets.mp4 3.8 MB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/2. Setting the Stage/1. Module Overview.mp4 3.8 MB
F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/4. Assessing Model Explainability/2. Microsofts Guiding Principles for Responsible AI.mp4 3.8 MB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/exercise.7z 3.8 MB
A2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/1. Course Overview/1. Course Overview.mp4 3.7 MB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/6. Managing Azure Data Explorer/2. Managing ADX Database Permissions.mp4 3.7 MB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/1. Course Overview/1. Course Overview.mp4 3.7 MB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/16. Demo - Parts-of-speech.mp4 3.7 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/1. Course Overview/1. Course Overview.mp4 3.7 MB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/1. Course Overview/1. Course Overview.mp4 3.7 MB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/4. Recommending Next Steps Based on Available Data/6. Correlation vs. Causation.mp4 3.7 MB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/2. Setting the Stage/05. Measures of Variability.mp4 3.6 MB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/exercise.7z 3.6 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/5. Handling Missing Data/04. Problems in Deleting Rows.mp4 3.6 MB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/01. Module Overview.mp4 3.5 MB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/2. Setting the Stage/8. Specialized Roles in Data Science.mp4 3.5 MB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/2. Setting the Stage/10. Summary.mp4 3.5 MB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/2. Exploring Your Dataset for Feature Selection and Extraction/1. Exploring Your Dataset for Feature Selection and Extraction.mp4 3.5 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/4. Preparing Input Data for Machine Learning Models/11. Entropy-based Discretization.mp4 3.5 MB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/2. Exploring Your Dataset for Feature Selection and Extraction/4. Determining the Feature Structure Appropriate for the Algorithm.mp4 3.4 MB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/6. Managing Azure Data Explorer/3. ADX Health and Performance Monitoring.mp4 3.4 MB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/3. Communicating Barriers to Data Access to Stakeholders/5. Personally Identifiable Information (PII).mp4 3.4 MB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/5. Processing Categorical or Text Feature Sets/5. t-SNE.mp4 3.4 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/8. Identify Data-level Issues In Machine Learning Models/5. Multicollinearity Problem in Regression Models.mp4 3.4 MB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/21. Module Summary.mp4 3.4 MB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/08. Demo - Token Cleaning.mp4 3.4 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/2. Getting Started with Azure Machine Learning/3. Introduction to Azure Machine Learning.mp4 3.4 MB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/1. Course Overview/1. Course Overview.mp4 3.4 MB
F3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/2. Deploying a Machine Learning Model/4. Deploying Models.mp4 3.3 MB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/2. Setting the Stage/7. Data Science Services and Tools in Azure.mp4 3.3 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/8. Identify Data-level Issues In Machine Learning Models/4. Data Scale Issues in Distance-based Models.mp4 3.3 MB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/4. Recommending Next Steps Based on Available Data/5. Testing for Validity.mp4 3.3 MB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/4. Recommending Next Steps Based on Available Data/2. A New Problem to Be Solved.mp4 3.3 MB
F1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/1. Course Overview/1. Course Overview.mp4 3.3 MB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/4. Recommending Next Steps Based on Available Data/4. The Complete Media Insights Solution.mp4 3.3 MB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/5. Reviewing Results with Stakeholders/2. Communicating Knowledge and Insights.mp4 3.3 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/8. Identify Data-level Issues In Machine Learning Models/6. Outliers in Regression Models.mp4 3.3 MB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/3. Communicating Barriers to Data Access to Stakeholders/4. The Budget Barrier and Solution.mp4 3.3 MB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/4. Defining Normalization and Standardization Techniques/05. Demo - Binning.mp4 3.3 MB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/3. Performing Feature Extraction/3. Creating and Using Feature Extraction Algorithms.mp4 3.3 MB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/01. Module Overview.mp4 3.2 MB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/2. Setting the Stage/01. Module Overview.mp4 3.2 MB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/2. Azure Data Explorer (ADX) Overview/7. ADX Pricing.mp4 3.2 MB
D1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/1. Course Overview/1. Course Overview.mp4 3.2 MB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/4. Defining Normalization and Standardization Techniques/02. Common Scaling Approaches.mp4 3.2 MB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/05. Meet the Stereotypical Technical Players.mp4 3.2 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/2. Getting Started with Azure Machine Learning/4. Azure Machine Learning Experiment Workflow.mp4 3.2 MB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/5. Data Ingestion for ADX/5. EventGrids Overview.mp4 3.1 MB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/1. Course Overview/1. Course Overview.mp4 3.1 MB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/6. Managing Azure Data Explorer/4. Scaling the ADX Cluster.mp4 3.1 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/6. Role of Feature Engineering in Machine Learning/6. Feature Engineering Categorical Variables.mp4 3.1 MB
E5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/1. Course Overview/1. Course Overview.mp4 3.1 MB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/3. Quantifying the Business Problem/08. Data Science Project Risks.mp4 3.1 MB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/2. Setting the Stage/06. Modality and Skewness.mp4 3.1 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/5. Handling Missing Data/07. Disadvantages of Single Imputation Methods.mp4 3.1 MB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/06. Identify Your Stakeholders.mp4 3.1 MB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/5. Leveraging Nominal Data in Machine Learning/6. Demo - Label and One-hot Encoding.mp4 3.1 MB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/04. Preprocessing and NLP.mp4 3.0 MB
B3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/3. Wrangling Data/5. Multiple Data Sets.mp4 3.0 MB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/5. Data Ingestion for ADX/3. Data Ingestion in Azure.mp4 3.0 MB
F3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/1. Course Overview/1. Course Overview.mp4 3.0 MB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/3. Approaching Normalization and Standardization/3. Outlier Detection and Imputation.mp4 3.0 MB
B3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/1. Course Overview/1. Course Overview.mp4 3.0 MB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/19. N-grams.mp4 2.9 MB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/1. Course Overview/1. Course Overview.mp4 2.9 MB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/3. Communicating Barriers to Data Access to Stakeholders/3. Your New Data Broker.mp4 2.9 MB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/06. TF-IDF Encoding.mp4 2.9 MB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/3. Communicating Barriers to Data Access to Stakeholders/6. Ethical and Legal Barriers to Data Use.mp4 2.9 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/6. Role of Feature Engineering in Machine Learning/3. Role of Feature Engineering in Model Complexity.mp4 2.8 MB
B2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/1. Course Overview/1. Course Overview.mp4 2.8 MB
F3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/4. Managing a Models Lifecycle/3. Long Term Planning.mp4 2.8 MB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/2. Azure Data Explorer (ADX) Overview/5. Azure Data Explorer Features.mp4 2.8 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/6. Role of Feature Engineering in Machine Learning/4. Build Better Models with Feature Engineering.mp4 2.8 MB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/4. Performing Feature Normalization/2. Understanding Feature Normalization.mp4 2.8 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/5. Handling Missing Data/06. Replace with Mean, Median, and Mode.mp4 2.7 MB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/02. What Is the Project Motivation Factor.mp4 2.7 MB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/2. Reviewing Available Data with Stakeholders/2. The Use Case - Media Insights Solution.mp4 2.7 MB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/3. Approaching Normalization and Standardization/6. Standardization and Normalization.mp4 2.7 MB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/2. Reviewing Available Data with Stakeholders/3. Available Data Sources.mp4 2.7 MB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/2. Reviewing Available Data with Stakeholders/6. The Format of the Data.mp4 2.7 MB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/2. Understanding How Feature Set Complexity Impacts Model Quality/2. Feature Set Complexity.mp4 2.6 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/4. Preparing Input Data for Machine Learning Models/09. Demo - Reducing Data (Attribute Sampling).mp4 2.6 MB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/4. Defining Normalization and Standardization Techniques/10. Summary.mp4 2.6 MB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/12. Assessing Stakehoders Needs.mp4 2.6 MB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/6. Going beyond PCA to Reduce Complexity in Numeric Feature Sets/1. Introduction and Module Overview.mp4 2.6 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/8. Identify Data-level Issues In Machine Learning Models/2. Imbalanced Dataset for Classification Problems.mp4 2.6 MB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/6. Final Takeaway/1. Final Takeaway.mp4 2.5 MB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/1. Course Overview/1. Course Overview.mp4 2.5 MB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/4. Using Principal Component Analysis to Reduce Numeric Feature Sets/7. Summary.mp4 2.5 MB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/1. Course Overview/1. Course Overview.mp4 2.5 MB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/5. Performing Feature Selection/2. Understanding Feature Selection.mp4 2.5 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/6. Role of Feature Engineering in Machine Learning/5. Feature Engineering Numeric Variables.mp4 2.5 MB
A2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/2. Authentication and Authorization Methods/3. The Key Valet Pattern.mp4 2.5 MB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/3. Communicating Barriers to Data Access to Stakeholders/2. The Problem with the Internal Data.mp4 2.5 MB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/10. Asking the Right Questions.mp4 2.4 MB
B2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Identifying Potential Data Sources/6. Azure Open Datasets.mp4 2.4 MB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/4. Defining Normalization and Standardization Techniques/01. Module Overview.mp4 2.4 MB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/5. Processing Categorical or Text Feature Sets/1. Introduction and Module Overview.mp4 2.4 MB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/3. Approaching Normalization and Standardization/7. Demo - Normalize and Standardize in Python.mp4 2.4 MB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/5. Performing Feature Selection/1. Performing Feature Selection.mp4 2.3 MB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/16. Summary.mp4 2.3 MB
A2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/exercise.7z 2.3 MB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/2. Setting the Stage/04. Measures of Central Tendency.mp4 2.3 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/3. Differentiating Data, Features, Targets, and Models/2. Moving from Raw Data to Features.mp4 2.3 MB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/2. Setting the Stage/02. Is This Course for You.mp4 2.2 MB
F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/3. Improving Model Performance/3. Azure Automated Machine Learning.mp4 2.2 MB
E5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/2. Exploring Computer Vision on Azure/1. Overview.mp4 2.2 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/5. Handling Missing Data/09. How MICE Works.mp4 2.2 MB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/4. Recommending Next Steps Based on Available Data/7. The Big Ask.mp4 2.2 MB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/14. Project Gap Analysis.mp4 2.2 MB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/5. Reviewing Results with Stakeholders/5. Summary.mp4 2.2 MB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/13. Stemming.mp4 2.1 MB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/4. Using the Kusto Query Language (KQL)/1. Module Overview.mp4 2.1 MB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/5. Leveraging Nominal Data in Machine Learning/8. Summary.mp4 2.1 MB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/3. Performing Feature Extraction/1. What Is Feature Extraction.mp4 2.1 MB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/2. Reviewing Available Data with Stakeholders/4. Bring in the SME.mp4 2.1 MB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/4. Defining Normalization and Standardization Techniques/04. Binning.mp4 2.1 MB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/09. Stopword Removal.mp4 2.1 MB
A2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/4. Assessing Ethical and Legal Data Compliance/2. Encryption in Azure.mp4 2.1 MB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/5. Leveraging Nominal Data in Machine Learning/4. Problems with Categorical Data.mp4 2.1 MB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/17. Lemmatization.mp4 2.1 MB
F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/2. Evaluating Model Effectiveness/1. Overview.mp4 2.1 MB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/3. Applying Criteria-based Feature Reduction Techniques/2. Reasons For Feature Elimination.mp4 2.0 MB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/13. Accessing the Data.mp4 2.0 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/5. Handling Missing Data/02. Reasons Why Data Is Missing.mp4 2.0 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/7. Split a Data Set into Training and Testing Subsets/1. Introduction.mp4 2.0 MB
F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/1. Course Overview/1. Course Overview.mp4 2.0 MB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/15. Parts-of-speech Tagging.mp4 2.0 MB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/6. Going beyond PCA to Reduce Complexity in Numeric Feature Sets/7. Summary.mp4 2.0 MB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/11. Frequency Filtering.mp4 1.9 MB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/4. Defining Normalization and Standardization Techniques/08. Z-score.mp4 1.9 MB
E5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/4. Leveraging Convolutional Neural Networks for Feature Extraction/1. Overview.mp4 1.9 MB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/exercise.7z 1.9 MB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/5. Leveraging Nominal Data in Machine Learning/1. Module Overview.mp4 1.9 MB
B2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Identifying Potential Data Sources/1. Intro.mp4 1.9 MB
D1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/2. Determining Which Tools to Use/1. Introduction.mp4 1.9 MB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/3. Quantifying the Business Problem/09. Data Science Project Lifecycle.mp4 1.9 MB
F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/4. Assessing Model Explainability/6. Summary.mp4 1.8 MB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/6. Managing Azure Data Explorer/1. Module Overview.mp4 1.8 MB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/2. Reviewing Available Data with Stakeholders/5. Exploratory Data Analysis Tools.mp4 1.8 MB
F3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/exercise.7z 1.8 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/7. Split a Data Set into Training and Testing Subsets/7. Leave-one-out Cross Validation.mp4 1.8 MB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/2. Understanding How Feature Set Complexity Impacts Model Quality/7. Summary.mp4 1.8 MB
F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/2. Evaluating Model Effectiveness/6. Summary.mp4 1.8 MB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/18. Establishing Agreement to Proceed the Project Further.mp4 1.8 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/4. Preparing Input Data for Machine Learning Models/12. Demo - Entropy-based Discretization.mp4 1.8 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/4. Preparing Input Data for Machine Learning Models/01. Introduction.mp4 1.8 MB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/5. Leveraging Nominal Data in Machine Learning/2. Data Types in Statistics.mp4 1.7 MB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/3. Approaching Normalization and Standardization/1. Introduction.mp4 1.7 MB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/17. Address and Capture Any Concerns.mp4 1.7 MB
F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/3. Improving Model Performance/6. Summary.mp4 1.7 MB
E5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/2. Exploring Computer Vision on Azure/6. Summary.mp4 1.7 MB
E5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/3. Utilizing the SIFT and HOG Algorithms for Feature Detection/1. Overview.mp4 1.7 MB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/2. Setting the Stage/03. Skills Recommended for This Course.mp4 1.7 MB
D1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/4. Using the New Development Environment/6. Course Review.mp4 1.7 MB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/5. Performing Feature Selection/7. Takeaway.mp4 1.7 MB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/3. Approaching Normalization and Standardization/8. Summary.mp4 1.7 MB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/5. Data Ingestion for ADX/4. EventHubs Overview.mp4 1.7 MB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/3. Working with Azure Databricks/1. Module Overview.mp4 1.7 MB
A2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/3. Determining Data Availability/6. Availability on Other Azure Services.mp4 1.7 MB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/2. Setting the Stage/07. Kurtosis.mp4 1.6 MB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/4. Recommending Next Steps Based on Available Data/8. Summary.mp4 1.6 MB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/08. Get to Know Your Stakeholders.mp4 1.6 MB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/3. Applying Criteria-based Feature Reduction Techniques/8. Summary.mp4 1.6 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/3. Differentiating Data, Features, Targets, and Models/6. How Algorithms Learn Models.mp4 1.6 MB
E5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/4. Leveraging Convolutional Neural Networks for Feature Extraction/6. Summary.mp4 1.6 MB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/4. Recommending Next Steps Based on Available Data/1. Overview.mp4 1.6 MB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/6. Managing Azure Data Explorer/8. Summary.mp4 1.6 MB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/11. Capturing Stakeholders Needs.mp4 1.6 MB
B3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Transforming Data into Usable Datasets/1. Intro.mp4 1.5 MB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/4. Using the Kusto Query Language (KQL)/9. Summary.mp4 1.5 MB
E5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/3. Utilizing the SIFT and HOG Algorithms for Feature Detection/7. Summary.mp4 1.5 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/3. Differentiating Data, Features, Targets, and Models/8. Summary.mp4 1.5 MB
F3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/2. Deploying a Machine Learning Model/1. Intro.mp4 1.5 MB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/4. Using the Kusto Query Language (KQL)/4. Available KQL Demo Platforms.mp4 1.5 MB
B2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/3. Extracting and Loading Data into an Azure Workflow/7. Review.mp4 1.5 MB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/2. Reviewing Available Data with Stakeholders/7. Summary.mp4 1.5 MB
F1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/exercise.7z 1.5 MB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/4. Defining Normalization and Standardization Techniques/06. Heteroscedasticity.mp4 1.5 MB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/09. Managing the Initial Stakeholder Engagement.mp4 1.5 MB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/5. Processing Categorical or Text Feature Sets/7. Summary.mp4 1.5 MB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/5. Data Ingestion for ADX/8. Summary.mp4 1.4 MB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/3. Communicating Barriers to Data Access to Stakeholders/7. Summary.mp4 1.4 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/5. Handling Missing Data/01. Introduction.mp4 1.4 MB
A2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/3. Determining Data Availability/4. Azure SQL Datawarehouse Availability.mp4 1.4 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/7. Split a Data Set into Training and Testing Subsets/8. Summary.mp4 1.4 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/6. Role of Feature Engineering in Machine Learning/9. Summary.mp4 1.4 MB
B3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/3. Wrangling Data/6. Review.mp4 1.4 MB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/2. Reviewing Available Data with Stakeholders/1. Overview.mp4 1.4 MB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/2. Setting the Stage/9. Summary.mp4 1.4 MB
F3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/4. Managing a Models Lifecycle/2. Performance Analytics.mp4 1.4 MB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/2. Exploring Your Dataset for Feature Selection and Extraction/6. Takeaway.mp4 1.4 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/8. Identify Data-level Issues In Machine Learning Models/8. Summary.mp4 1.3 MB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/4. Model Evaluation and Summarizing Results/1. Module Overview.mp4 1.3 MB
F3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/4. Managing a Models Lifecycle/5. Review.mp4 1.3 MB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/3. Communicating Barriers to Data Access to Stakeholders/1. Overview.mp4 1.3 MB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/4. Using the Kusto Query Language (KQL)/3. Schema Mapping in KQL.mp4 1.3 MB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/4. Performing Feature Normalization/8. Takeaway.mp4 1.3 MB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/5. Reviewing Results with Stakeholders/1. Module Overview.mp4 1.3 MB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/15. Present the Proposal.mp4 1.2 MB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/03. Hard-skills and Soft-skills.mp4 1.2 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/8. Identify Data-level Issues In Machine Learning Models/1. Introduction.mp4 1.2 MB
A2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/4. Assessing Ethical and Legal Data Compliance/4. Module Summary.mp4 1.2 MB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/3. Quantifying the Business Problem/01. Access the Need of the Project to the Business.mp4 1.2 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/2. Getting Started with Azure Machine Learning/9. Summary.mp4 1.2 MB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/exercise.7z 1.2 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/6. Role of Feature Engineering in Machine Learning/1. Introduction.mp4 1.2 MB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/3. Building the ADX Environment/7. Summary.mp4 1.2 MB
A2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/2. Authentication and Authorization Methods/6. Module Summary.mp4 1.2 MB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/3. Building the ADX Environment/1. Module Overview.mp4 1.1 MB
D1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/4. Using the New Development Environment/1. Overview.mp4 1.1 MB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/2. Azure Data Explorer (ADX) Overview/8. Summary.mp4 1.1 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/3. Differentiating Data, Features, Targets, and Models/1. Introduction.mp4 1.1 MB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/5. Leveraging Nominal Data in Machine Learning/5. One-hot Encoding.mp4 1.1 MB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/4. Preparing Input Data for Machine Learning Models/13. Summary.mp4 1.1 MB
D1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/exercise.7z 1.1 MB
A2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/3. Determining Data Availability/9. Module Summary.mp4 1.1 MB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/5. Data Ingestion for ADX/1. Module Overview.mp4 1.1 MB
F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/3. Improving Model Performance/1. Overview.mp4 1.0 MB
D1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/3. Setting up a Development Environment/1. Introduction.mp4 1015.4 KB
B2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/3. Extracting and Loading Data into an Azure Workflow/1. Intro.mp4 981.9 KB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/4. Model Evaluation and Summarizing Results/7. Summary.mp4 975.5 KB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/3. Quantifying the Business Problem/03. Low Risk Data Science Project Scope.mp4 963.1 KB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/3. Working with Azure Databricks/8. Summary.mp4 928.7 KB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/16. Accessing the Teams Reaction.mp4 856.2 KB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/3. Quantifying the Business Problem/04. External vs. Internal Facing Project Scope.mp4 838.9 KB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/3. Performing Feature Extraction/6. Takeaway.mp4 823.8 KB
B3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Transforming Data into Usable Datasets/9. Review.mp4 793.3 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/5. Handling Missing Data/10. Summary.mp4 784.8 KB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/3. Quantifying the Business Problem/02. High Risk Data Science Project Scope.mp4 768.6 KB
F3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/3. Using Continuous Integration and Continuous Deployment/1. Intro.mp4 767.0 KB
F3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/4. Managing a Models Lifecycle/1. Intro.mp4 700.2 KB
B3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/3. Wrangling Data/1. Intro.mp4 669.3 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/4. Preparing Input Data for Machine Learning Models/02. Data Preprocessing Methods.mp4 645.9 KB
F3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/2. Deploying a Machine Learning Model/6. Review.mp4 640.6 KB
F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/4. Assessing Model Explainability/1. Overview.mp4 620.2 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/2. Getting Started with Azure Machine Learning/5. Prerequisites.mp4 567.0 KB
F3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/3. Using Continuous Integration and Continuous Deployment/5. Review.mp4 537.5 KB
scr 2022-08.png 493.3 KB
F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/exercise.7z 449.8 KB
D1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/2. Determining Which Tools to Use/4. Tools.vtt 30.7 KB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/3. Working with Azure Databricks/5. Demo - Azure Databricks with Azure Data Lake Storage Gen2.vtt 22.9 KB
F3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/3. Using Continuous Integration and Continuous Deployment/4. Creating Pipelines.vtt 21.9 KB
B3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Transforming Data into Usable Datasets/2. Data Structures.vtt 18.8 KB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/4. Using the Kusto Query Language (KQL)/6. Demo - Working with KQL - Timeseries.vtt 18.2 KB
F3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/2. Deploying a Machine Learning Model/5. Creating and Deploying.vtt 18.2 KB
E5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/3. Utilizing the SIFT and HOG Algorithms for Feature Detection/4. Extracting and Matching Features with SIFT.vtt 18.1 KB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/4. Using the Kusto Query Language (KQL)/5. Demo - Working with KQL - Basic.vtt 17.9 KB
B2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Identifying Potential Data Sources/2. Data.vtt 16.9 KB
D1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/2. Determining Which Tools to Use/2. Introduction to Data Science.vtt 15.7 KB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/3. Working with Azure Databricks/4. Demo - Configuring and Working with Azure Databricks.vtt 14.6 KB
E5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/4. Leveraging Convolutional Neural Networks for Feature Extraction/3. Convolutional Neural Network Overview.vtt 14.5 KB
E5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/3. Utilizing the SIFT and HOG Algorithms for Feature Detection/3. SIFT Introduction.vtt 14.3 KB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/4. Model Evaluation and Summarizing Results/6. Demo - Create Model and Perform Predictive Analytics Part3.vtt 13.9 KB
E5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/4. Leveraging Convolutional Neural Networks for Feature Extraction/5. Creating a CNN for Classification.vtt 13.3 KB
B3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Transforming Data into Usable Datasets/7. Bad Data.vtt 12.8 KB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/4. Using Principal Component Analysis to Reduce Numeric Feature Sets/3. How PCA Works.vtt 12.6 KB
E5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/4. Leveraging Convolutional Neural Networks for Feature Extraction/2. Neural Network Overview.vtt 12.4 KB
E5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/2. Exploring Computer Vision on Azure/2. Introduction to Computer Vision.vtt 12.3 KB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/5. Reviewing Results with Stakeholders/4. Demo - Communicating Insights using MatPlotLib.vtt 12.1 KB
B2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Identifying Potential Data Sources/4. Azure Data Catalog.vtt 11.7 KB
F1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/3. Import and Prepare Data for Modeling/1. Creating and Registering Microsoft AzureML Datastore.vtt 11.7 KB
D1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/3. Setting up a Development Environment/2. Provisioning an Environment.vtt 11.6 KB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/5. Data Ingestion for ADX/6. Demo - Data Ingestion Using EventHubs and .Net Custom Code.vtt 11.6 KB
E5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/3. Utilizing the SIFT and HOG Algorithms for Feature Detection/5. HOG Introduction.vtt 11.5 KB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/4. Model Evaluation and Summarizing Results/5. Demo - Create Model and Perform Predictive Analytics Part 2.vtt 11.0 KB
B2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/3. Extracting and Loading Data into an Azure Workflow/4. Azure Data Factory Demo.vtt 10.9 KB
D1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/2. Determining Which Tools to Use/3. Identifying Constraints.vtt 10.9 KB
B3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Transforming Data into Usable Datasets/5. Large Data Sets.vtt 10.8 KB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/3. Performing Feature Extraction/2. Performing Feature Extraction.vtt 10.6 KB
E5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/2. Exploring Computer Vision on Azure/3. Approaches to Computer Vision.vtt 10.4 KB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/5. Reviewing Results with Stakeholders/3. Demo - Communicating Insights using Power BI.vtt 10.2 KB
E5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/3. Utilizing the SIFT and HOG Algorithms for Feature Detection/6. Extracting and Matching Features with HOG.vtt 10.2 KB
F1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/5. Tuning Hyperparameters and AutoML/1. Hyperparameter Tuning - Theory.vtt 10.1 KB
F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/2. Evaluating Model Effectiveness/4. Demo - Inspecting an Azure ML Pipeline.vtt 10.1 KB
B2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/3. Extracting and Loading Data into an Azure Workflow/2. Extracting and Loading.vtt 10.1 KB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/3. Building the ADX Environment/4. Demo - Create ADX Cluster Using PowerShell.vtt 9.9 KB
F1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/5. Tuning Hyperparameters and AutoML/3. Automated Machine Learning Experiment Using Python SDK.vtt 9.9 KB
E5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/3. Utilizing the SIFT and HOG Algorithms for Feature Detection/2. Image Processing Techniques.vtt 9.9 KB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/3. Working with Azure Databricks/6. Demo - Performing Exploratory Data Analysis using Azure Databricks.vtt 9.9 KB
B2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/3. Extracting and Loading Data into an Azure Workflow/3. Azure Data Factory.vtt 9.9 KB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/3. Working with Azure Databricks/7. Demo - Working with Streaming Data Using Azure Databricks and Event Hubs.vtt 9.8 KB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/6. Managing Azure Data Explorer/5. Demo - Managing ADX Database Permissions.vtt 9.8 KB
A2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/2. Authentication and Authorization Methods/4. Access Keys and SAS.vtt 9.5 KB
B2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Identifying Potential Data Sources/3. Discovering Data.vtt 9.5 KB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/3. Approaching Normalization and Standardization/4. Demo - Outlier Detection in Python.vtt 9.5 KB
F1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/4. Training, Tracking, and Monitoring a Model/4. Training Script and Estimators in AzureML.vtt 9.4 KB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/5. Data Ingestion for ADX/7. Demo - Data Ingestion Using EventGrids and Blob Storage.vtt 9.4 KB
F1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/3. Import and Prepare Data for Modeling/2. Data Preprocessing with Microsoft AzureML.vtt 9.4 KB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/15. Demo - Word Embeddings with BERT on AMLS.vtt 9.1 KB
A2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/3. Determining Data Availability/3. Azure SQL High Availability.vtt 9.0 KB
B2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/3. Extracting and Loading Data into an Azure Workflow/6. HD Insights Demo.vtt 8.8 KB
B3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/3. Wrangling Data/2. Aggregation.vtt 8.7 KB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/4. Using the Kusto Query Language (KQL)/8. Demo - Data Sharing and Visualization Using Power BI.vtt 8.7 KB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/6. Managing Azure Data Explorer/7. Demo - Scaling the ADX Cluster.vtt 8.5 KB
F1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/2. Understanding Azure Machine Learning service/1. Overview of Microsoft Azure Machine Learning service.vtt 8.4 KB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/6. Managing Azure Data Explorer/6. Demo - ADX Health and Performance Monitoring.vtt 8.4 KB
E5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/2. Exploring Computer Vision on Azure/4. Azure Services for Computer Vision.vtt 8.3 KB
A2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/2. Authentication and Authorization Methods/2. Authentication and Authorization on Azure.vtt 8.3 KB
D1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/4. Using the New Development Environment/2. Running a Test Experiment.vtt 8.2 KB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/5. Performing Feature Selection/4. Fisher Linear Discriminant Analysis Demo.vtt 7.9 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/4. Preparing Input Data for Machine Learning Models/03. Demo - Exploratory Data Analysis.vtt 7.8 KB
F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/3. Improving Model Performance/4. Demo - Creating an Automated ML Experiment.vtt 7.8 KB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/2. Setting the Stage/08. Gaussian Distributions.vtt 7.8 KB
B3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Transforming Data into Usable Datasets/4. Python Demo.vtt 7.8 KB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/4. Using the Kusto Query Language (KQL)/7. Demo - Data Obfuscation in KQL.vtt 7.8 KB
F1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/4. Training, Tracking, and Monitoring a Model/1. Setting up Compute Target.vtt 7.8 KB
E5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/4. Leveraging Convolutional Neural Networks for Feature Extraction/4. Working with MNIST.vtt 7.7 KB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/3. Performing Feature Extraction/4. Performing Feature Extraction on Unstructured Text.vtt 7.7 KB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/4. Model Evaluation and Summarizing Results/4. Demo - Create Model and Perform Predictive Analytics Part 1.vtt 7.7 KB
D1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/4. Using the New Development Environment/3. Iris Demo.vtt 7.7 KB
A2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/4. Assessing Ethical and Legal Data Compliance/1. Ethical and Legal Compliance.vtt 7.5 KB
A2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/3. Determining Data Availability/8. Azure Availability Features.vtt 7.5 KB
F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/2. Evaluating Model Effectiveness/5. Demo - Scoring and Evaluating the Pipeline Model.vtt 7.4 KB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/3. Performing Feature Extraction/5. Demo - Human Face or Not Human Face.vtt 7.4 KB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/11. Demo - The Hashing Trick.vtt 7.4 KB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/2. Understanding How Feature Set Complexity Impacts Model Quality/6. Demo.vtt 7.4 KB
F1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/4. Training, Tracking, and Monitoring a Model/2. Metrics Logging in AzureML.vtt 7.4 KB
B2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/3. Extracting and Loading Data into an Azure Workflow/5. HD Insights.vtt 7.4 KB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/2. Understanding How Feature Set Complexity Impacts Model Quality/1. Introduction and Module Overview.vtt 7.3 KB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/03. One-hot and Count Vector Encoding.vtt 7.1 KB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/3. Building the ADX Environment/3. Create ADX Cluster Using PowerShell.vtt 7.1 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/3. Differentiating Data, Features, Targets, and Models/3. 6 Characteristics of a Good Feature.vtt 7.1 KB
D1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/4. Using the New Development Environment/4. Saving Work.vtt 7.0 KB
E5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/2. Exploring Computer Vision on Azure/5. Launching a Notebook Instance.vtt 7.0 KB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/2. Azure Data Explorer (ADX) Overview/6. Azure Data Explorer Capabilities.vtt 6.9 KB
F1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/5. Tuning Hyperparameters and AutoML/4. Automated Machine Learning Experiment Using Visual Interface.vtt 6.9 KB
F1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/2. Understanding Azure Machine Learning service/2. Creating and Deleting Workspace.vtt 6.7 KB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/2. Exploring Your Dataset for Feature Selection and Extraction/3. Exploring Your Data and Identifying the Distribution of Your Da.vtt 6.7 KB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/2. Exploring Your Dataset for Feature Selection and Extraction/5. Dataset Exploration Demo.vtt 6.7 KB
B3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/3. Wrangling Data/4. Data Labeling.vtt 6.6 KB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/5. Performing Feature Selection/6. Compute Linear Correlation Demo.vtt 6.5 KB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/4. Model Evaluation and Summarizing Results/2. Model Training Process.vtt 6.5 KB
B3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Transforming Data into Usable Datasets/3. R Demo.vtt 6.4 KB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/3. Building the ADX Environment/6. Demo - Create ADX Cluster Using Command Line Interface.vtt 6.4 KB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/12. Demo - Frequency Filtering.vtt 6.2 KB
A2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/3. Determining Data Availability/1. Data Availability Concepts.vtt 6.2 KB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/3. Working with Azure Databricks/3. Understanding Apache Spark and Notebook.vtt 6.1 KB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/13. Demo - Locality-sensitive Hashing.vtt 6.1 KB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/10. Demo - Stopword Removal.vtt 6.1 KB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/2. Exploring Your Dataset for Feature Selection and Extraction/2. What Is a Feature in Machine Learning.vtt 6.0 KB
F3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/3. Using Continuous Integration and Continuous Deployment/2. Tracking Models.vtt 5.9 KB
D1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/3. Setting up a Development Environment/5. Environment Management.vtt 5.9 KB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/2. Understanding How Feature Set Complexity Impacts Model Quality/5. How Feature Set Complexity Impacts Model Interpretability.vtt 5.8 KB
F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/4. Assessing Model Explainability/4. Setting Up an Experiment in a Jupyter Notebook.vtt 5.8 KB
B2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Identifying Potential Data Sources/5. Azure Data Catalog Demo.vtt 5.7 KB
F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/2. Evaluating Model Effectiveness/3. Scoring and Evaluating an Azure ML Pipeline.vtt 5.6 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/2. Getting Started with Azure Machine Learning/6. Demo - Creating an Azure Machine Learning Studio Workspace.vtt 5.6 KB
F1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/5. Tuning Hyperparameters and AutoML/2. Hyperparameter Tuning - Demo.vtt 5.6 KB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/3. Approaching Normalization and Standardization/2. Machine Learning Process Distilled.vtt 5.6 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/7. Split a Data Set into Training and Testing Subsets/5. Demo - Cross-validation.vtt 5.6 KB
A2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/2. Authentication and Authorization Methods/1. The Shared Responsibility Model.vtt 5.6 KB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/2. Setting the Stage/2. Data Science Overview.vtt 5.6 KB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/3. Building the ADX Environment/2. Demo - Create ADX Cluster Using Azure Portal.vtt 5.5 KB
F1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/2. Understanding Azure Machine Learning service/3. Setting up Environments.vtt 5.5 KB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/03. Demo - Configure AMLS.vtt 5.5 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/playlist.m3u 5.5 KB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/06. Demo - Sentence and Word Tokenization.vtt 5.4 KB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/5. Processing Categorical or Text Feature Sets/3. LDA.vtt 5.4 KB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/2. Setting the Stage/09. Calculating the Mean, Median, and Mode.vtt 5.4 KB
F1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/3. Import and Prepare Data for Modeling/3. Microsoft AzureML Datasets.vtt 5.4 KB
A2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/3. Determining Data Availability/5. Cosmos DB Availability.vtt 5.3 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/7. Split a Data Set into Training and Testing Subsets/4. Splitting Data for Model Tuning.vtt 5.3 KB
D1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/4. Using the New Development Environment/5. Azure Backup Services Demo.vtt 5.3 KB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/04. Demo - Bag-of-words.vtt 5.3 KB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/5. Processing Categorical or Text Feature Sets/2. How Can You Process Categorical or Text Feature Sets.vtt 5.3 KB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/3. Applying Criteria-based Feature Reduction Techniques/5. Bivariate Techniques.vtt 5.2 KB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/5. Performing Feature Selection/5. Permutation Feature Importance Demo.vtt 5.2 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/3. Differentiating Data, Features, Targets, and Models/4. Define Target for ML Problems.vtt 5.2 KB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/14. BERT.vtt 5.1 KB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/2. Setting the Stage/6. Microsofts Team Data Science Process.vtt 5.1 KB
F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/4. Assessing Model Explainability/5. Demo - Touring the Azure Python Interpretability SDK.vtt 5.1 KB
A2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/2. Authentication and Authorization Methods/5. Demo - Working with Tokens.vtt 5.1 KB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/2. Azure Data Explorer (ADX) Overview/4. Data Exploration in Azure (ADX).vtt 5.0 KB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/02. Encoding Text as Numbers.vtt 5.0 KB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/4. Performing Feature Normalization/7. Encoding Features Demo.vtt 5.0 KB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/5. Leveraging Nominal Data in Machine Learning/3. Data Measurement Scales.vtt 5.0 KB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/09. Demo - Word Embeddings Using Word2Vec.vtt 4.9 KB
D1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/3. Setting up a Development Environment/4. Version Management.vtt 4.9 KB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/2. Azure Data Explorer (ADX) Overview/3. Data Exploration and Visualization.vtt 4.9 KB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/2. Setting the Stage/3. Understanding the Modeling Process.vtt 4.9 KB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/4. Recommending Next Steps Based on Available Data/3. Synthetic Training Data.vtt 4.9 KB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/07. Demo - TF-IDF Encoding.vtt 4.9 KB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/10. Feature Hashing.vtt 4.8 KB
F3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/2. Deploying a Machine Learning Model/2. Azure Machine Learning.vtt 4.8 KB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/05. Tokenization and Cleaning.vtt 4.8 KB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/4. Using Principal Component Analysis to Reduce Numeric Feature Sets/6. Demo.vtt 4.7 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/4. Preparing Input Data for Machine Learning Models/07. Demo - Data Transformation.vtt 4.7 KB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/3. Quantifying the Business Problem/05. Defining Business Metrics.vtt 4.7 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/6. Role of Feature Engineering in Machine Learning/8. Demo - Learning with Counts Categorical Variables.vtt 4.7 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/7. Split a Data Set into Training and Testing Subsets/2. Demo - Training and Testing on Same Data.vtt 4.7 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/6. Role of Feature Engineering in Machine Learning/7. Demo - One-hot Encoding Categorical Variables.vtt 4.7 KB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/3. Working with Azure Databricks/2. Understanding the Azure Databricks Ecosystem.vtt 4.7 KB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/04. Identifying the Hard-skills.vtt 4.6 KB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/3. Approaching Normalization and Standardization/5. Demo - Imputation in Python.vtt 4.5 KB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/2. Setting the Stage/05. Measures of Variability.vtt 4.5 KB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/2. Azure Data Explorer (ADX) Overview/1. Module Overview.vtt 4.5 KB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/3. Applying Criteria-based Feature Reduction Techniques/3. Univariate Techniques.vtt 4.5 KB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/07. Demo - NLTK Tokenizers.vtt 4.4 KB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/4. Using Principal Component Analysis to Reduce Numeric Feature Sets/4. KPCA.vtt 4.4 KB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/4. Performing Feature Normalization/3. Clip Values Demo.vtt 4.4 KB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/20. Demo - N-grams.vtt 4.4 KB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/3. Applying Criteria-based Feature Reduction Techniques/7. Demo.vtt 4.4 KB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/3. Quantifying the Business Problem/10. Quantifying the Risks for the Data Science Project.vtt 4.4 KB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/3. Applying Criteria-based Feature Reduction Techniques/4. How Statistical Tests Work.vtt 4.4 KB
F1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/4. Training, Tracking, and Monitoring a Model/5. Distributed Training in AzureML.vtt 4.4 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/7. Split a Data Set into Training and Testing Subsets/3. Demo - Split Data into Training and Test Set.vtt 4.4 KB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/02. Prerequisites.vtt 4.3 KB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/3. Approaching Normalization and Standardization/3. Outlier Detection and Imputation.vtt 4.3 KB
F3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/4. Managing a Models Lifecycle/4. Application Insights.vtt 4.3 KB
~i.txt 4.3 KB
F3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/3. Using Continuous Integration and Continuous Deployment/3. Continuous Deployment.vtt 4.3 KB
A2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/3. Determining Data Availability/7. High Quality Datasets.vtt 4.3 KB
B3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Transforming Data into Usable Datasets/8. Handling Bad Data.vtt 4.3 KB
B3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Transforming Data into Usable Datasets/6. SQL Data Sampling.vtt 4.3 KB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/4. Performing Feature Normalization/4. Group Data into Bins Demo.vtt 4.3 KB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/5. Data Ingestion for ADX/2. KQL Schema Mapping for Data Ingestion.vtt 4.3 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/2. Getting Started with Azure Machine Learning/2. What Is Machine Learning.vtt 4.2 KB
D1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/3. Setting up a Development Environment/3. Creating a DSVM.vtt 4.2 KB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/4. Model Evaluation and Summarizing Results/3. Model Training Techniques.vtt 4.2 KB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/6. Going beyond PCA to Reduce Complexity in Numeric Feature Sets/3. How k-means Works.vtt 4.1 KB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/2. Setting the Stage/10. Summary.vtt 4.1 KB
B3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/3. Wrangling Data/5. Multiple Data Sets.vtt 4.1 KB
F3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/2. Deploying a Machine Learning Model/3. Creating Azure Machine Learning.vtt 4.1 KB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/4. Defining Normalization and Standardization Techniques/03. Demo - Common Scaling Approaches.vtt 4.0 KB
A2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/3. Determining Data Availability/2. Availability on Blob Storage.vtt 4.0 KB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/2. Setting the Stage/06. Modality and Skewness.vtt 4.0 KB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/2. Setting the Stage/1. Module Overview.vtt 3.9 KB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/2. Setting the Stage/01. Module Overview.vtt 3.9 KB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/2. Azure Data Explorer (ADX) Overview/2. Data Science Overview.vtt 3.9 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/7. Split a Data Set into Training and Testing Subsets/6. Demo - Model Selection.vtt 3.8 KB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/6. Going beyond PCA to Reduce Complexity in Numeric Feature Sets/4. Autoencoders.vtt 3.8 KB
F3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/4. Managing a Models Lifecycle/3. Long Term Planning.vtt 3.8 KB
F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/2. Evaluating Model Effectiveness/2. Preliminary Terminology.vtt 3.8 KB
F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/4. Assessing Model Explainability/3. Unintended Bias and Interpretability.vtt 3.7 KB
B3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/3. Wrangling Data/3. Azure Notebooks Demo.vtt 3.7 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/2. Getting Started with Azure Machine Learning/8. Demo - Exploring the Dataset.vtt 3.7 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/8. Identify Data-level Issues In Machine Learning Models/6. Outliers in Regression Models.vtt 3.7 KB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/4. Performing Feature Normalization/6. Principal Component Analysis Demo.vtt 3.7 KB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/5. Performing Feature Selection/3. Fliter Based Feature Selection Demo.vtt 3.7 KB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/5. Processing Categorical or Text Feature Sets/4. Dictionary Learning.vtt 3.7 KB
F3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/2. Deploying a Machine Learning Model/4. Deploying Models.vtt 3.7 KB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/2. Setting the Stage/5. Model Evaluation.vtt 3.6 KB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/4. Using Principal Component Analysis to Reduce Numeric Feature Sets/5. PCA Limitations.vtt 3.6 KB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/14. Demo - Stemming.vtt 3.6 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/8. Identify Data-level Issues In Machine Learning Models/7. Problem with High-dimensional Datasets.vtt 3.6 KB
F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/3. Improving Model Performance/2. Detecting and Preventing Overfitting.vtt 3.6 KB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/4. Defining Normalization and Standardization Techniques/02. Common Scaling Approaches.vtt 3.6 KB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/6. Going beyond PCA to Reduce Complexity in Numeric Feature Sets/6. Demo.vtt 3.6 KB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/3. Applying Criteria-based Feature Reduction Techniques/1. Introduction and Module Overview.vtt 3.6 KB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/5. Processing Categorical or Text Feature Sets/6. Demo.vtt 3.6 KB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/playlist.m3u 3.5 KB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/4. Using the Kusto Query Language (KQL)/2. Understanding the Kusto Query Language (KQL).vtt 3.5 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/2. Getting Started with Azure Machine Learning/1. Introduction.vtt 3.5 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/2. Getting Started with Azure Machine Learning/3. Introduction to Azure Machine Learning.vtt 3.4 KB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/4. Defining Normalization and Standardization Techniques/09. Demo - Z-score.vtt 3.4 KB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/12. Locality-sensitive Hashing.vtt 3.4 KB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/2. Setting the Stage/8. Specialized Roles in Data Science.vtt 3.4 KB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/3. Quantifying the Business Problem/07. Managing Technical Metrics.vtt 3.4 KB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/3. Building the ADX Environment/5. Create ADX Cluster Using Command Line Interface.vtt 3.4 KB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/01. Module Overview.vtt 3.4 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/4. Preparing Input Data for Machine Learning Models/11. Entropy-based Discretization.vtt 3.4 KB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/2. Understanding How Feature Set Complexity Impacts Model Quality/3. Sources of Model Error.vtt 3.4 KB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/4. Using Principal Component Analysis to Reduce Numeric Feature Sets/1. Introduction and Module Overview.vtt 3.4 KB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/08. Word Embeddings.vtt 3.3 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/4. Preparing Input Data for Machine Learning Models/10. Demo - Discretizing Data.vtt 3.3 KB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/1. Course Overview/1. Course Overview.vtt 3.3 KB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/5. Leveraging Nominal Data in Machine Learning/7. Demo - Label Encoding and XGBoost.vtt 3.3 KB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/01. What Is Data Science.vtt 3.3 KB
F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/4. Assessing Model Explainability/2. Microsofts Guiding Principles for Responsible AI.vtt 3.3 KB
A2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/4. Assessing Ethical and Legal Data Compliance/3. Demo - Azure Security, Privacy, and Compliance.vtt 3.3 KB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/21. Module Summary.vtt 3.2 KB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/4. Performing Feature Normalization/5. Normalize Data Demo.vtt 3.2 KB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/3. Applying Criteria-based Feature Reduction Techniques/6. Multivariate Techniques.vtt 3.2 KB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/3. Quantifying the Business Problem/06. Business Metrics Classifications.vtt 3.2 KB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/4. Using Principal Component Analysis to Reduce Numeric Feature Sets/2. PCA.vtt 3.2 KB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/2. Setting the Stage/4. Model Training.vtt 3.2 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/6. Role of Feature Engineering in Machine Learning/2. Why Feature Engineering.vtt 3.2 KB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/01. Module Overview.vtt 3.1 KB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/3. Communicating Barriers to Data Access to Stakeholders/5. Personally Identifiable Information (PII).vtt 3.1 KB
B2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Identifying Potential Data Sources/6. Azure Open Datasets.vtt 3.1 KB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/4. Defining Normalization and Standardization Techniques/07. Demo - Correcting Heteroscedasticity.vtt 3.1 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/3. Differentiating Data, Features, Targets, and Models/7. Demo - Modifying the Metadata of Datasets.vtt 3.1 KB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/playlist.m3u 3.1 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/2. Getting Started with Azure Machine Learning/7. Demo - Creating an Azure Machine Learning Service Workspace.vtt 3.1 KB
F1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/4. Training, Tracking, and Monitoring a Model/3. Setting up Run Object.vtt 3.1 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/5. Handling Missing Data/03. Demo - Listwise Deletion.vtt 3.1 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/4. Preparing Input Data for Machine Learning Models/05. Demo - Data Cleaning (Outliers).vtt 3.1 KB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/2. Setting the Stage/7. Data Science Services and Tools in Azure.vtt 3.1 KB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/3. Performing Feature Extraction/3. Creating and Using Feature Extraction Algorithms.vtt 3.1 KB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/05. Meet the Stereotypical Technical Players.vtt 3.0 KB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/04. Preprocessing and NLP.vtt 3.0 KB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/3. Quantifying the Business Problem/08. Data Science Project Risks.vtt 3.0 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/2. Getting Started with Azure Machine Learning/4. Azure Machine Learning Experiment Workflow.vtt 3.0 KB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/4. Recommending Next Steps Based on Available Data/6. Correlation vs. Causation.vtt 3.0 KB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/4. Recommending Next Steps Based on Available Data/4. The Complete Media Insights Solution.vtt 3.0 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/5. Handling Missing Data/04. Problems in Deleting Rows.vtt 3.0 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/8. Identify Data-level Issues In Machine Learning Models/4. Data Scale Issues in Distance-based Models.vtt 3.0 KB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/4. Recommending Next Steps Based on Available Data/2. A New Problem to Be Solved.vtt 2.9 KB
B2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Identifying Potential Data Sources/7. Azure Open Datasets Demo.vtt 2.9 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/5. Handling Missing Data/06. Replace with Mean, Median, and Mode.vtt 2.9 KB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/4. Defining Normalization and Standardization Techniques/10. Summary.vtt 2.9 KB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/6. Going beyond PCA to Reduce Complexity in Numeric Feature Sets/2. k-means Model Stacking.vtt 2.9 KB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/5. Leveraging Nominal Data in Machine Learning/6. Demo - Label and One-hot Encoding.vtt 2.9 KB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/02. What Is the Project Motivation Factor.vtt 2.9 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/6. Role of Feature Engineering in Machine Learning/4. Build Better Models with Feature Engineering.vtt 2.9 KB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/3. Approaching Normalization and Standardization/6. Standardization and Normalization.vtt 2.8 KB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/4. Performing Feature Normalization/2. Understanding Feature Normalization.vtt 2.8 KB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/6. Going beyond PCA to Reduce Complexity in Numeric Feature Sets/5. Manifold Learning.vtt 2.8 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/6. Role of Feature Engineering in Machine Learning/3. Role of Feature Engineering in Model Complexity.vtt 2.8 KB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/18. Demo - Lemmatization.vtt 2.8 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/8. Identify Data-level Issues In Machine Learning Models/5. Multicollinearity Problem in Regression Models.vtt 2.8 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/5. Handling Missing Data/07. Disadvantages of Single Imputation Methods.vtt 2.8 KB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/4. Defining Normalization and Standardization Techniques/01. Module Overview.vtt 2.8 KB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/playlist.m3u 2.8 KB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/05. Demo - Bag-of-n-grams.vtt 2.8 KB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/playlist.m3u 2.8 KB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/5. Leveraging Nominal Data in Machine Learning/8. Summary.vtt 2.8 KB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/2. Reviewing Available Data with Stakeholders/2. The Use Case - Media Insights Solution.vtt 2.8 KB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/19. N-grams.vtt 2.8 KB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/2. Setting the Stage/04. Measures of Central Tendency.vtt 2.7 KB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/1. Course Overview/1. Course Overview.vtt 2.7 KB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/2. Setting the Stage/02. Is This Course for You.vtt 2.7 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/4. Preparing Input Data for Machine Learning Models/08. Demo - Reducing Data (Record Sampling).vtt 2.7 KB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/5. Data Ingestion for ADX/3. Data Ingestion in Azure.vtt 2.7 KB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/3. Communicating Barriers to Data Access to Stakeholders/4. The Budget Barrier and Solution.vtt 2.7 KB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/6. Final Takeaway/1. Final Takeaway.vtt 2.7 KB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/4. Recommending Next Steps Based on Available Data/5. Testing for Validity.vtt 2.7 KB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/2. Reviewing Available Data with Stakeholders/6. The Format of the Data.vtt 2.7 KB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/07. Selecting the Right Stakeholders.vtt 2.7 KB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/06. Identify Your Stakeholders.vtt 2.7 KB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/06. TF-IDF Encoding.vtt 2.7 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/6. Role of Feature Engineering in Machine Learning/6. Feature Engineering Categorical Variables.vtt 2.6 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/4. Preparing Input Data for Machine Learning Models/04. Demo - Data Cleaning (Erroneous Data).vtt 2.6 KB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/playlist.m3u 2.6 KB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/2. Understanding How Feature Set Complexity Impacts Model Quality/4. Measuring and Detecting Problems Due to Feature Set Complexity.vtt 2.6 KB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/5. Performing Feature Selection/2. Understanding Feature Selection.vtt 2.6 KB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/5. Performing Feature Selection/1. Performing Feature Selection.vtt 2.6 KB
A2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/2. Authentication and Authorization Methods/3. The Key Valet Pattern.vtt 2.6 KB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/5. Data Ingestion for ADX/5. EventGrids Overview.vtt 2.6 KB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/4. Defining Normalization and Standardization Techniques/05. Demo - Binning.vtt 2.6 KB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/5. Reviewing Results with Stakeholders/2. Communicating Knowledge and Insights.vtt 2.6 KB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/10. Asking the Right Questions.vtt 2.5 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/8. Identify Data-level Issues In Machine Learning Models/2. Imbalanced Dataset for Classification Problems.vtt 2.5 KB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/3. Communicating Barriers to Data Access to Stakeholders/3. Your New Data Broker.vtt 2.5 KB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/6. Managing Azure Data Explorer/3. ADX Health and Performance Monitoring.vtt 2.5 KB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/2. Azure Data Explorer (ADX) Overview/5. Azure Data Explorer Features.vtt 2.5 KB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/2. Setting the Stage/03. Skills Recommended for This Course.vtt 2.4 KB
C2. Experimental Design for Data Analysis (Janani Ravi, 2019)/~i.txt 2.4 KB
A2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/1. Course Overview/1. Course Overview.vtt 2.4 KB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/6. Managing Azure Data Explorer/2. Managing ADX Database Permissions.vtt 2.4 KB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/1. Course Overview/1. Course Overview.vtt 2.4 KB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/2. Exploring Your Dataset for Feature Selection and Extraction/1. Exploring Your Dataset for Feature Selection and Extraction.vtt 2.4 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/5. Handling Missing Data/09. How MICE Works.vtt 2.4 KB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/4. Defining Normalization and Standardization Techniques/08. Z-score.vtt 2.4 KB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/1. Course Overview/1. Course Overview.vtt 2.4 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/5. Handling Missing Data/05. Demo - Using Indicator Variables.vtt 2.4 KB
F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/3. Improving Model Performance/3. Azure Automated Machine Learning.vtt 2.3 KB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/3. Communicating Barriers to Data Access to Stakeholders/6. Ethical and Legal Barriers to Data Use.vtt 2.3 KB
D1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/4. Using the New Development Environment/6. Course Review.vtt 2.3 KB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/4. Defining Normalization and Standardization Techniques/04. Binning.vtt 2.3 KB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/3. Building Features Around Text Data for Use in Machine Learning Models/16. Summary.vtt 2.3 KB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/2. Reviewing Available Data with Stakeholders/3. Available Data Sources.vtt 2.3 KB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/6. Managing Azure Data Explorer/4. Scaling the ADX Cluster.vtt 2.3 KB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/2. Setting the Stage/07. Kurtosis.vtt 2.3 KB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/3. Quantifying the Business Problem/09. Data Science Project Lifecycle.vtt 2.3 KB
F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/3. Improving Model Performance/5. Demo - Interpreting the Experiment Results.vtt 2.3 KB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/08. Demo - Token Cleaning.vtt 2.3 KB
C1. Summarizing Data and Deducing Probabilities (Janani Ravi, 2021)/~i.txt 2.3 KB
D1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/2. Determining Which Tools to Use/1. Introduction.vtt 2.3 KB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/2. Exploring Your Dataset for Feature Selection and Extraction/4. Determining the Feature Structure Appropriate for the Algorithm.vtt 2.2 KB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/3. Approaching Normalization and Standardization/1. Introduction.vtt 2.2 KB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/~i.txt 2.2 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/3. Differentiating Data, Features, Targets, and Models/2. Moving from Raw Data to Features.vtt 2.2 KB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/3. Communicating Barriers to Data Access to Stakeholders/2. The Problem with the Internal Data.vtt 2.2 KB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/5. Leveraging Nominal Data in Machine Learning/1. Module Overview.vtt 2.2 KB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/5. Leveraging Nominal Data in Machine Learning/4. Problems with Categorical Data.vtt 2.2 KB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/3. Approaching Normalization and Standardization/7. Demo - Normalize and Standardize in Python.vtt 2.2 KB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/playlist.m3u 2.2 KB
E5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/2. Exploring Computer Vision on Azure/1. Overview.vtt 2.2 KB
B1. Representing, Processing, and Preparing Data (Janani Ravi, 2019)/~i.txt 2.2 KB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/5. Leveraging Nominal Data in Machine Learning/2. Data Types in Statistics.vtt 2.2 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/8. Identify Data-level Issues In Machine Learning Models/3. Demo - SMOTE.vtt 2.2 KB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/1. Course Overview/1. Course Overview.vtt 2.1 KB
B4. Combining and Shaping Data (Janani Ravi, 2020)/~i.txt 2.1 KB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/3. Approaching Normalization and Standardization/8. Summary.vtt 2.1 KB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/playlist.m3u 2.1 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/6. Role of Feature Engineering in Machine Learning/5. Feature Engineering Numeric Variables.vtt 2.1 KB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/2. Understanding How Feature Set Complexity Impacts Model Quality/2. Feature Set Complexity.vtt 2.1 KB
A2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/4. Assessing Ethical and Legal Data Compliance/2. Encryption in Azure.vtt 2.1 KB
E5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/4. Leveraging Convolutional Neural Networks for Feature Extraction/1. Overview (1).vtt 2.1 KB
E5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/4. Leveraging Convolutional Neural Networks for Feature Extraction/1. Overview.vtt 2.1 KB
E5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/1. Course Overview/1. Course Overview.vtt 2.1 KB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/4. Recommending Next Steps Based on Available Data/7. The Big Ask.vtt 2.1 KB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/~i.txt 2.1 KB
F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/3. Improving Model Performance/6. Summary.vtt 2.1 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/5. Handling Missing Data/08. Demo - Replace with MICE.vtt 2.1 KB
F1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/1. Course Overview/1. Course Overview.vtt 2.1 KB
B2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/1. Course Overview/1. Course Overview.vtt 2.1 KB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/2. Reviewing Available Data with Stakeholders/5. Exploratory Data Analysis Tools.vtt 2.0 KB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/5. Processing Categorical or Text Feature Sets/5. t-SNE.vtt 2.0 KB
B2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Identifying Potential Data Sources/1. Intro.vtt 2.0 KB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/13. Stemming.vtt 2.0 KB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/~i.txt 2.0 KB
C5. Communicating Data Insights (Janani Ravi, 2020)/~i.txt 2.0 KB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/14. Project Gap Analysis.vtt 2.0 KB
E5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/3. Utilizing the SIFT and HOG Algorithms for Feature Detection/1. Overview.vtt 2.0 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/7. Split a Data Set into Training and Testing Subsets/1. Introduction.vtt 2.0 KB
E5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/2. Exploring Computer Vision on Azure/6. Summary.vtt 2.0 KB
E5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/3. Utilizing the SIFT and HOG Algorithms for Feature Detection/7. Summary.vtt 2.0 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/1. Course Overview/1. Course Overview.vtt 2.0 KB
F3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/1. Course Overview/1. Course Overview.vtt 2.0 KB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/16. Demo - Parts-of-speech.vtt 2.0 KB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/12. Assessing Stakehoders Needs.vtt 1.9 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/4. Preparing Input Data for Machine Learning Models/06. Demo - Data Cleaning (Duplicate Rows).vtt 1.9 KB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/1. Course Overview/1. Course Overview.vtt 1.9 KB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/17. Lemmatization.vtt 1.9 KB
A2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/3. Determining Data Availability/6. Availability on Other Azure Services.vtt 1.9 KB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/~i.txt 1.9 KB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/1. Course Overview/1. Course Overview.vtt 1.9 KB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/4. Defining Normalization and Standardization Techniques/06. Heteroscedasticity.vtt 1.9 KB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/4. Using Principal Component Analysis to Reduce Numeric Feature Sets/7. Summary.vtt 1.9 KB
F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/2. Evaluating Model Effectiveness/6. Summary.vtt 1.9 KB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/3. Performing Feature Extraction/1. What Is Feature Extraction.vtt 1.9 KB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/5. Processing Categorical or Text Feature Sets/1. Introduction and Module Overview.vtt 1.9 KB
F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/4. Assessing Model Explainability/6. Summary.vtt 1.9 KB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/4. Using the Kusto Query Language (KQL)/1. Module Overview.vtt 1.8 KB
E5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/~i.txt 1.8 KB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/2. Reviewing Available Data with Stakeholders/4. Bring in the SME.vtt 1.8 KB
F3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/2. Deploying a Machine Learning Model/1. Intro.vtt 1.8 KB
B3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/1. Course Overview/1. Course Overview.vtt 1.8 KB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/~i.txt 1.8 KB
D3. Building, Training, and Validating Models in Microsoft Azure (Bismark Adomako, 2020)/~i.txt 1.8 KB
F1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/~i.txt 1.8 KB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/5. Performing Feature Selection/7. Takeaway.vtt 1.8 KB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/5. Reviewing Results with Stakeholders/5. Summary.vtt 1.8 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/3. Differentiating Data, Features, Targets, and Models/5. Demo - Exploring Datasets for Different Problems.vtt 1.8 KB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/13. Accessing the Data.vtt 1.8 KB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/playlist.m3u 1.8 KB
E5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/4. Leveraging Convolutional Neural Networks for Feature Extraction/6. Summary.vtt 1.8 KB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/17. Address and Capture Any Concerns.vtt 1.8 KB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/6. Managing Azure Data Explorer/1. Module Overview.vtt 1.8 KB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/1. Course Overview/1. Course Overview.vtt 1.8 KB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/3. Applying Criteria-based Feature Reduction Techniques/2. Reasons For Feature Elimination.vtt 1.8 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/7. Split a Data Set into Training and Testing Subsets/7. Leave-one-out Cross Validation.vtt 1.8 KB
B3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/3. Wrangling Data/6. Review.vtt 1.8 KB
B3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Transforming Data into Usable Datasets/1. Intro.vtt 1.8 KB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/03. Hard-skills and Soft-skills.vtt 1.8 KB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/11. Frequency Filtering.vtt 1.8 KB
B2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/3. Extracting and Loading Data into an Azure Workflow/7. Review.vtt 1.7 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/~i.txt 1.7 KB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/2. Azure Data Explorer (ADX) Overview/7. ADX Pricing.vtt 1.7 KB
F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/2. Evaluating Model Effectiveness/1. Overview.vtt 1.7 KB
C3. Interpreting Data with Statistical Models (Axel Sirota, 2020)/~i.txt 1.7 KB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/6. Going beyond PCA to Reduce Complexity in Numeric Feature Sets/1. Introduction and Module Overview.vtt 1.7 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/5. Handling Missing Data/02. Reasons Why Data Is Missing.vtt 1.7 KB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/~i.txt 1.7 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/3. Differentiating Data, Features, Targets, and Models/6. How Algorithms Learn Models.vtt 1.7 KB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/~i.txt 1.7 KB
C4. Interpreting Data with Advanced Statistical Models (Axel Sirota, 2019)/~i.txt 1.7 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/4. Preparing Input Data for Machine Learning Models/01. Introduction.vtt 1.7 KB
E5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)/playlist.m3u 1.6 KB
A2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/3. Determining Data Availability/4. Azure SQL Datawarehouse Availability.vtt 1.6 KB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/18. Establishing Agreement to Proceed the Project Further.vtt 1.6 KB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/~i.txt 1.6 KB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/6. Managing Azure Data Explorer/8. Summary.vtt 1.6 KB
D1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/~i.txt 1.6 KB
A2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/~i.txt 1.6 KB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/4. Recommending Next Steps Based on Available Data/8. Summary.vtt 1.6 KB
A2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/4. Assessing Ethical and Legal Data Compliance/4. Module Summary.vtt 1.5 KB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/4. Performing Feature Normalization/1. Performing Feature Normalization.vtt 1.5 KB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/3. Working with Azure Databricks/1. Module Overview.vtt 1.5 KB
D1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/1. Course Overview/1. Course Overview.vtt 1.5 KB
F3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/4. Managing a Models Lifecycle/5. Review.vtt 1.5 KB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/2. Reviewing Available Data with Stakeholders/7. Summary.vtt 1.5 KB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/4. Recommending Next Steps Based on Available Data/1. Overview.vtt 1.5 KB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/09. Stopword Removal.vtt 1.5 KB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/6. Going beyond PCA to Reduce Complexity in Numeric Feature Sets/7. Summary.vtt 1.5 KB
D1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/4. Using the New Development Environment/1. Overview.vtt 1.5 KB
F3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/~i.txt 1.5 KB
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)/2. Processing and Simplifying Text to Simplify Feature Creation/15. Parts-of-speech Tagging.vtt 1.5 KB
F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/~i.txt 1.5 KB
F3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/4. Managing a Models Lifecycle/2. Performance Analytics.vtt 1.5 KB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/08. Get to Know Your Stakeholders.vtt 1.4 KB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/2. Reviewing Available Data with Stakeholders/1. Overview.vtt 1.4 KB
B2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/~i.txt 1.4 KB
B3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/~i.txt 1.4 KB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/5. Data Ingestion for ADX/4. EventHubs Overview.vtt 1.4 KB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/09. Managing the Initial Stakeholder Engagement.vtt 1.4 KB
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)/5. Leveraging Nominal Data in Machine Learning/5. One-hot Encoding.vtt 1.4 KB
A2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/playlist.m3u 1.4 KB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/2. Exploring Your Dataset for Feature Selection and Extraction/6. Takeaway.vtt 1.4 KB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/3. Communicating Barriers to Data Access to Stakeholders/7. Summary.vtt 1.4 KB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/5. Data Ingestion for ADX/8. Summary.vtt 1.3 KB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/4. Model Evaluation and Summarizing Results/1. Module Overview.vtt 1.3 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/5. Handling Missing Data/01. Introduction.vtt 1.3 KB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/4. Using the Kusto Query Language (KQL)/4. Available KQL Demo Platforms.vtt 1.3 KB
F1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)/playlist.m3u 1.3 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/3. Differentiating Data, Features, Targets, and Models/8. Summary.vtt 1.3 KB
F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/1. Course Overview/1. Course Overview.vtt 1.3 KB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/2. Setting the Stage/9. Summary.vtt 1.3 KB
F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/playlist.m3u 1.3 KB
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/4. Performing Feature Normalization/8. Takeaway.vtt 1.3 KB
A2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/3. Determining Data Availability/9. Module Summary.vtt 1.3 KB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/2. Understanding How Feature Set Complexity Impacts Model Quality/7. Summary.vtt 1.3 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/3. Differentiating Data, Features, Targets, and Models/1. Introduction.vtt 1.2 KB
D1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/3. Setting up a Development Environment/1. Introduction.vtt 1.2 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/8. Identify Data-level Issues In Machine Learning Models/8. Summary.vtt 1.2 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/7. Split a Data Set into Training and Testing Subsets/8. Summary.vtt 1.2 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/6. Role of Feature Engineering in Machine Learning/9. Summary.vtt 1.2 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/2. Getting Started with Azure Machine Learning/9. Summary.vtt 1.2 KB
A2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)/2. Authentication and Authorization Methods/6. Module Summary.vtt 1.2 KB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/3. Applying Criteria-based Feature Reduction Techniques/8. Summary.vtt 1.2 KB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/5. Reviewing Results with Stakeholders/1. Module Overview.vtt 1.1 KB
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)/3. Communicating Barriers to Data Access to Stakeholders/1. Overview.vtt 1.1 KB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/4. Using the Kusto Query Language (KQL)/3. Schema Mapping in KQL.vtt 1.1 KB
F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/3. Improving Model Performance/1. Overview.vtt 1.1 KB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/4. Using the Kusto Query Language (KQL)/9. Summary.vtt 1.1 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/8. Identify Data-level Issues In Machine Learning Models/1. Introduction.vtt 1.1 KB
B2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/3. Extracting and Loading Data into an Azure Workflow/1. Intro.vtt 1.1 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/6. Role of Feature Engineering in Machine Learning/1. Introduction.vtt 1.1 KB
F3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/playlist.m3u 1.1 KB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/2. Azure Data Explorer (ADX) Overview/8. Summary.vtt 1.0 KB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/11. Capturing Stakeholders Needs.vtt 1.0 KB
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/4. Model Evaluation and Summarizing Results/7. Summary.vtt 1.0 KB
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/3. Building the ADX Environment/1. Module Overview.vtt 1.0 KB
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/3. Quantifying the Business Problem/01. Access the Need of the Project to the Business.vtt 1.0 KB
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)/5. Processing Categorical or Text Feature Sets/7. Summary.vtt 1.0 KB
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/4. Preparing Input Data for Machine Learning Models/13. Summary.vtt 1018 bytes
B3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/2. Transforming Data into Usable Datasets/9. Review.vtt 1013 bytes
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/4. Preparing Input Data for Machine Learning Models/09. Demo - Reducing Data (Attribute Sampling).vtt 1012 bytes
B2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)/playlist.m3u 1007 bytes
D1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)/playlist.m3u 997 bytes
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/5. Handling Missing Data/10. Summary.vtt 985 bytes
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/3. Building the ADX Environment/7. Summary.vtt 984 bytes
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)/5. Data Ingestion for ADX/1. Module Overview.vtt 969 bytes
B3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/3. Wrangling Data/1. Intro.vtt 913 bytes
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/3. Quantifying the Business Problem/04. External vs. Internal Facing Project Scope.vtt 878 bytes
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)/3. Performing Feature Extraction/6. Takeaway.vtt 853 bytes
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/3. Quantifying the Business Problem/02. High Risk Data Science Project Scope.vtt 836 bytes
B3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)/playlist.m3u 820 bytes
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/4. Preparing Input Data for Machine Learning Models/12. Demo - Entropy-based Discretization.vtt 818 bytes
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/3. Quantifying the Business Problem/03. Low Risk Data Science Project Scope.vtt 807 bytes
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/15. Present the Proposal.vtt 801 bytes
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)/3. Working with Azure Databricks/8. Summary.vtt 791 bytes
F3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/3. Using Continuous Integration and Continuous Deployment/1. Intro.vtt 790 bytes
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/2. Getting Started with Azure Machine Learning/5. Prerequisites.vtt 779 bytes
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)/2. Determining if Data Science Is an Appropriate Fit for the Organization/16. Accessing the Teams Reaction.vtt 737 bytes
F3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/2. Deploying a Machine Learning Model/6. Review.vtt 705 bytes
F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)/4. Assessing Model Explainability/1. Overview.vtt 671 bytes
F3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/4. Managing a Models Lifecycle/1. Intro.vtt 662 bytes
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)/4. Preparing Input Data for Machine Learning Models/02. Data Preprocessing Methods.vtt 658 bytes
F3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)/3. Using Continuous Integration and Continuous Deployment/5. Review.vtt 533 bytes

Pluralsight Path. Data Science with Microsoft Azure (2021)

File List

Download Torrent

Related Resources

Copyright Infringement