[GigaCourse.Com] Udemy - The Data Science Course Complete Data Science Bootcamp

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16 - Statistics - Practical Example Descriptive Statistics/001 Practical Example Descriptive Statistics.mp4 150.2 MB
11 - Probability - Bayesian Inference/012 A Practical Example of Bayesian Inference.mp4 139.1 MB
12 - Probability - Distributions/015 A Practical Example of Probability Distributions.mp4 138.1 MB
05 - The Field of Data Science - Popular Data Science Techniques/001 Techniques for Working with Traditional Data.mp4 107.4 MB
40 - Part 6 Mathematics/011 Why is Linear Algebra Useful.mp4 88.4 MB
35 - Advanced Statistical Methods - Practical Example Linear Regression/001 Practical Example Linear Regression (Part 1).mp4 84.7 MB
20 - Statistics - Hypothesis Testing/001 Null vs Alternative Hypothesis.mp4 83.6 MB
64 - Appendix - Working with Text Files in Python/018 Importing Data from .json Files.mp4 82.0 MB
05 - The Field of Data Science - Popular Data Science Techniques/010 Types of Machine Learning.mp4 80.6 MB
05 - The Field of Data Science - Popular Data Science Techniques/007 Techniques for Working with Traditional Methods.mp4 76.1 MB
64 - Appendix - Working with Text Files in Python/013 Importing .csv Files - Part III.mp4 75.0 MB
51 - Deep Learning - Business Case Example/004 Business Case Preprocessing the Data.mp4 73.9 MB
58 - Case Study - Preprocessing the 'Absenteeism_data'/011 Obtaining Dummies from a Single Feature.mp4 69.8 MB
19 - Statistics - Practical Example Inferential Statistics/001 Practical Example Inferential Statistics.mp4 69.0 MB
56 - Software Integration/003 Taking a Closer Look at APIs.mp4 67.1 MB
55 - Appendix Deep Learning - TensorFlow 1 Business Case/004 Business Case Preprocessing.mp4 63.7 MB
05 - The Field of Data Science - Popular Data Science Techniques/003 Techniques for Working with Big Data.mp4 62.1 MB
55 - Appendix Deep Learning - TensorFlow 1 Business Case/001 Business Case Getting Acquainted with the Dataset.mp4 60.2 MB
56 - Software Integration/002 What are Data Connectivity, APIs, and Endpoints.mp4 60.2 MB
58 - Case Study - Preprocessing the 'Absenteeism_data'/016 Classifying the Various Reasons for Absence.mp4 59.2 MB
08 - The Field of Data Science - Debunking Common Misconceptions/001 Debunking Common Misconceptions.mp4 58.8 MB
02 - The Field of Data Science - The Various Data Science Disciplines/001 Data Science and Business Buzzwords Why are there so Many.mp4 57.4 MB
13 - Probability - Probability in Other Fields/003 Probability in Data Science.mp4 56.9 MB
64 - Appendix - Working with Text Files in Python/015 Importing Data with .loadtxt() and .genfromtxt().mp4 56.3 MB
55 - Appendix Deep Learning - TensorFlow 1 Business Case/006 Creating a Data Provider.mp4 56.3 MB
58 - Case Study - Preprocessing the 'Absenteeism_data'/003 Checking the Content of the Data Set.mp4 54.1 MB
05 - The Field of Data Science - Popular Data Science Techniques/005 Business Intelligence (BI) Techniques.mp4 52.9 MB
02 - The Field of Data Science - The Various Data Science Disciplines/003 Business Analytics, Data Analytics, and Data Science An Introduction.mp4 52.6 MB
18 - Statistics - Inferential Statistics Confidence Intervals/002 Confidence Intervals; Population Variance Known; Z-score.mp4 52.2 MB
01 - Part 1 Introduction/002 What Does the Course Cover.mp4 51.4 MB
51 - Deep Learning - Business Case Example/001 Business Case Exploring the Dataset and Identifying Predictors.mp4 51.3 MB
35 - Advanced Statistical Methods - Practical Example Linear Regression/008 Practical Example Linear Regression (Part 5).mp4 50.4 MB
64 - Appendix - Working with Text Files in Python/011 Importing .csv Files - Part I.mp4 49.9 MB
05 - The Field of Data Science - Popular Data Science Techniques/009 Machine Learning (ML) Techniques.mp4 49.4 MB
04 - The Field of Data Science - The Benefits of Each Discipline/001 The Reason Behind These Disciplines.mp4 46.7 MB
21 - Statistics - Practical Example Hypothesis Testing/001 Practical Example Hypothesis Testing.mp4 45.8 MB
09 - Part 2 Probability/002 Computing Expected Values.mp4 45.7 MB
54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/009 MNIST Results and Testing.mp4 45.5 MB
02 - The Field of Data Science - The Various Data Science Disciplines/005 A Breakdown of our Data Science Infographic.mp4 45.3 MB
60 - Case Study - Loading the 'absenteeism_module'/003 Deploying the 'absenteeism_module' - Part II.mp4 45.1 MB
18 - Statistics - Inferential Statistics Confidence Intervals/009 Confidence intervals. Two means. Dependent samples.mp4 45.0 MB
01 - Part 1 Introduction/001 A Practical Example What You Will Learn in This Course.mp4 43.9 MB
64 - Appendix - Working with Text Files in Python/016 Importing Data - Partial Cleaning While Importing Data.mp4 43.9 MB
51 - Deep Learning - Business Case Example/009 Business Case Setting an Early Stopping Mechanism.mp4 43.8 MB
62 - Appendix - Additional Python Tools/005 List Comprehensions.mp4 43.2 MB
15 - Statistics - Descriptive Statistics/001 Types of Data.mp4 43.2 MB
64 - Appendix - Working with Text Files in Python/021 Importing Data in Python - an Important Exercise.mp4 43.0 MB
64 - Appendix - Working with Text Files in Python/019 An Introduction to Working with Excel Files in Python.mp4 43.0 MB
56 - Software Integration/005 Software Integration - Explained.mp4 42.9 MB
10 - Probability - Combinatorics/011 A Practical Example of Combinatorics.mp4 42.8 MB
55 - Appendix Deep Learning - TensorFlow 1 Business Case/007 Business Case Model Outline.mp4 42.5 MB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/005 Splitting the Data for Training and Testing.mp4 41.9 MB
58 - Case Study - Preprocessing the 'Absenteeism_data'/007 Dropping a Column from a DataFrame in Python.mp4 41.3 MB
13 - Probability - Probability in Other Fields/001 Probability in Finance.mp4 40.3 MB
61 - Case Study - Analyzing the Predicted Outputs in Tableau/004 Analyzing Reasons vs Probability in Tableau.mp4 40.2 MB
58 - Case Study - Preprocessing the 'Absenteeism_data'/026 Analyzing the Dates from the Initial Data Set.mp4 40.1 MB
43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/004 Basic NN Example (Part 4).mp4 40.0 MB
35 - Advanced Statistical Methods - Practical Example Linear Regression/006 Practical Example Linear Regression (Part 4).mp4 39.4 MB
20 - Statistics - Hypothesis Testing/003 Rejection Region and Significance Level.mp4 38.7 MB
61 - Case Study - Analyzing the Predicted Outputs in Tableau/002 Analyzing Age vs Probability in Tableau.mp4 38.7 MB
12 - Probability - Distributions/010 Continuous Distributions The Standard Normal Distribution.mp4 38.4 MB
38 - Advanced Statistical Methods - K-Means Clustering/013 How is Clustering Useful.mp4 37.5 MB
09 - Part 2 Probability/003 Frequency.mp4 37.4 MB
63 - Appendix - pandas Fundamentals/010 Data Selection in pandas DataFrames.mp4 37.3 MB
02 - The Field of Data Science - The Various Data Science Disciplines/004 Continuing with BI, ML, and AI.mp4 36.9 MB
20 - Statistics - Hypothesis Testing/005 Test for the Mean. Population Variance Known.mp4 36.9 MB
15 - Statistics - Descriptive Statistics/003 Categorical Variables - Visualization Techniques.mp4 36.6 MB
12 - Probability - Distributions/008 Characteristics of Continuous Distributions.mp4 36.1 MB
37 - Advanced Statistical Methods - Cluster Analysis/002 Some Examples of Clusters.mp4 35.9 MB
12 - Probability - Distributions/002 Types of Probability Distributions.mp4 35.6 MB
34 - Advanced Statistical Methods - Linear Regression with sklearn/019 Train - Test Split Explained.mp4 35.6 MB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/006 Fitting the Model and Assessing its Accuracy.mp4 35.3 MB
14 - Part 3 Statistics/001 Population and Sample.mp4 35.1 MB
54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/004 MNIST Model Outline.mp4 34.6 MB
40 - Part 6 Mathematics/010 Dot Product of Matrices.mp4 34.3 MB
38 - Advanced Statistical Methods - K-Means Clustering/002 A Simple Example of Clustering.mp4 34.2 MB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/002 Adjusted R-Squared.mp4 34.2 MB
38 - Advanced Statistical Methods - K-Means Clustering/012 Market Segmentation with Cluster Analysis (Part 2).mp4 34.1 MB
58 - Case Study - Preprocessing the 'Absenteeism_data'/027 Extracting the Month Value from the Date Column.mp4 33.9 MB
20 - Statistics - Hypothesis Testing/007 p-value.mp4 33.8 MB
62 - Appendix - Additional Python Tools/004 Triple Nested For Loops.mp4 33.0 MB
22 - Part 4 Introduction to Python/004 Installing Python and Jupyter.mp4 32.8 MB
20 - Statistics - Hypothesis Testing/010 Test for the Mean. Dependent Samples.mp4 32.8 MB
50 - Deep Learning - Classifying on the MNIST Dataset/006 MNIST Preprocess the Data - Shuffle and Batch.mp4 32.7 MB
28 - Python - Sequences/005 Dictionaries.mp4 32.4 MB
63 - Appendix - pandas Fundamentals/011 pandas DataFrames - Indexing with .iloc[].mp4 32.2 MB
15 - Statistics - Descriptive Statistics/002 Levels of Measurement.mp4 32.2 MB
35 - Advanced Statistical Methods - Practical Example Linear Regression/002 Practical Example Linear Regression (Part 2).mp4 31.9 MB
54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/008 MNIST Learning.mp4 31.8 MB
34 - Advanced Statistical Methods - Linear Regression with sklearn/003 Simple Linear Regression with sklearn.mp4 31.6 MB
13 - Probability - Probability in Other Fields/002 Probability in Statistics.mp4 31.6 MB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/012 Testing the Model We Created.mp4 31.6 MB
50 - Deep Learning - Classifying on the MNIST Dataset/010 MNIST Learning.mp4 31.0 MB
12 - Probability - Distributions/006 Discrete Distributions The Binomial Distribution.mp4 30.6 MB
28 - Python - Sequences/002 Using Methods.mp4 30.4 MB
62 - Appendix - Additional Python Tools/006 Anonymous (Lambda) Functions.mp4 30.3 MB
63 - Appendix - pandas Fundamentals/009 pandas DataFrames - Common Attributes.mp4 29.8 MB
32 - Advanced Statistical Methods - Linear Regression with StatsModels/005 First Regression in Python.mp4 29.6 MB
09 - Part 2 Probability/001 The Basic Probability Formula.mp4 29.4 MB
34 - Advanced Statistical Methods - Linear Regression with sklearn/004 Simple Linear Regression with sklearn - A StatsModels-like Summary Table.mp4 28.9 MB
32 - Advanced Statistical Methods - Linear Regression with StatsModels/008 How to Interpret the Regression Table.mp4 28.7 MB
18 - Statistics - Inferential Statistics Confidence Intervals/001 What are Confidence Intervals.mp4 28.6 MB
64 - Appendix - Working with Text Files in Python/009 Importing Text Files - open().mp4 28.2 MB
38 - Advanced Statistical Methods - K-Means Clustering/011 Market Segmentation with Cluster Analysis (Part 1).mp4 28.1 MB
51 - Deep Learning - Business Case Example/008 Business Case Learning and Interpreting the Result.mp4 27.8 MB
05 - The Field of Data Science - Popular Data Science Techniques/011 Real Life Examples of Machine Learning (ML).mp4 27.7 MB
17 - Statistics - Inferential Statistics Fundamentals/008 Estimators and Estimates.mp4 27.7 MB
58 - Case Study - Preprocessing the 'Absenteeism_data'/010 Analyzing the Reasons for Absence.mp4 27.6 MB
17 - Statistics - Inferential Statistics Fundamentals/003 The Normal Distribution.mp4 27.5 MB
05 - The Field of Data Science - Popular Data Science Techniques/008 Real Life Examples of Traditional Methods.mp4 27.4 MB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/008 A3 Normality and Homoscedasticity.mp4 27.4 MB
58 - Case Study - Preprocessing the 'Absenteeism_data'/017 Using .concat() in Python.mp4 27.3 MB
44 - Deep Learning - TensorFlow 2.0 Introduction/001 How to Install TensorFlow 2.0.mp4 27.3 MB
51 - Deep Learning - Business Case Example/003 Business Case Balancing the Dataset.mp4 27.3 MB
55 - Appendix Deep Learning - TensorFlow 1 Business Case/003 The Importance of Working with a Balanced Dataset.mp4 27.3 MB
44 - Deep Learning - TensorFlow 2.0 Introduction/006 Outlining the Model with TensorFlow 2.mp4 26.9 MB
55 - Appendix Deep Learning - TensorFlow 1 Business Case/008 Business Case Optimization.mp4 26.9 MB
38 - Advanced Statistical Methods - K-Means Clustering/006 How to Choose the Number of Clusters.mp4 26.9 MB
64 - Appendix - Working with Text Files in Python/010 Importing Text Files - with open().mp4 26.3 MB
44 - Deep Learning - TensorFlow 2.0 Introduction/007 Interpreting the Result and Extracting the Weights and Bias.mp4 26.0 MB
09 - Part 2 Probability/004 Events and Their Complements.mp4 25.8 MB
39 - Advanced Statistical Methods - Other Types of Clustering/003 Heatmaps.mp4 25.7 MB
62 - Appendix - Additional Python Tools/001 Using the .format() Method.mp4 25.7 MB
36 - Advanced Statistical Methods - Logistic Regression/010 Binary Predictors in a Logistic Regression.mp4 24.8 MB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/008 Interpreting the Coefficients for Our Problem.mp4 24.8 MB
05 - The Field of Data Science - Popular Data Science Techniques/006 Real Life Examples of Business Intelligence (BI).mp4 24.7 MB
15 - Statistics - Descriptive Statistics/011 Mean, median and mode.mp4 24.5 MB
34 - Advanced Statistical Methods - Linear Regression with sklearn/015 Feature Selection through Standardization of Weights.mp4 24.5 MB
20 - Statistics - Hypothesis Testing/014 Test for the mean. Independent Samples (Part 2).mp4 24.5 MB
54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/006 Calculating the Accuracy of the Model.mp4 24.5 MB
63 - Appendix - pandas Fundamentals/005 Using .unique() and .nunique().mp4 24.3 MB
57 - Case Study - What's Next in the Course/003 Introducing the Data Set.mp4 24.2 MB
11 - Probability - Bayesian Inference/004 Union of Sets.mp4 24.2 MB
49 - Deep Learning - Preprocessing/005 Binary and One-Hot Encoding.mp4 23.9 MB
12 - Probability - Distributions/007 Discrete Distributions The Poisson Distribution.mp4 23.9 MB
36 - Advanced Statistical Methods - Logistic Regression/003 Logistic vs Logit Function.mp4 23.8 MB
45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/003 Digging into a Deep Net.mp4 23.7 MB
32 - Advanced Statistical Methods - Linear Regression with StatsModels/004 Python Packages Installation.mp4 23.7 MB
10 - Probability - Combinatorics/006 Solving Combinations.mp4 23.7 MB
29 - Python - Iterations/001 For Loops.mp4 23.6 MB
42 - Deep Learning - Introduction to Neural Networks/011 Optimization Algorithm 1-Parameter Gradient Descent.mp4 23.6 MB
15 - Statistics - Descriptive Statistics/015 Variance.mp4 23.5 MB
64 - Appendix - Working with Text Files in Python/026 Saving Your Data with NumPy - Part II - .npz.mp4 23.3 MB
17 - Statistics - Inferential Statistics Fundamentals/006 Central Limit Theorem.mp4 23.2 MB
18 - Statistics - Inferential Statistics Confidence Intervals/008 Margin of Error.mp4 23.1 MB
28 - Python - Sequences/001 Lists.mp4 23.0 MB
50 - Deep Learning - Classifying on the MNIST Dataset/004 MNIST Preprocess the Data - Create a Validation Set and Scale It.mp4 22.9 MB
50 - Deep Learning - Classifying on the MNIST Dataset/012 MNIST Testing the Model.mp4 22.6 MB
45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/004 Non-Linearities and their Purpose.mp4 22.6 MB
32 - Advanced Statistical Methods - Linear Regression with StatsModels/010 What is the OLS.mp4 22.5 MB
64 - Appendix - Working with Text Files in Python/022 Importing Data with the .squeeze() Method.mp4 22.4 MB
63 - Appendix - pandas Fundamentals/001 Introduction to pandas Series.mp4 22.2 MB
40 - Part 6 Mathematics/006 Addition and Subtraction of Matrices.mp4 22.1 MB
50 - Deep Learning - Classifying on the MNIST Dataset/008 MNIST Outline the Model.mp4 22.1 MB
29 - Python - Iterations/004 Conditional Statements and Loops.mp4 21.9 MB
36 - Advanced Statistical Methods - Logistic Regression/002 A Simple Example in Python.mp4 21.9 MB
56 - Software Integration/004 Communication between Software Products through Text Files.mp4 21.9 MB
34 - Advanced Statistical Methods - Linear Regression with sklearn/008 Calculating the Adjusted R-Squared in sklearn.mp4 21.7 MB
36 - Advanced Statistical Methods - Logistic Regression/015 Testing the Model.mp4 21.6 MB
11 - Probability - Bayesian Inference/011 Bayes' Law.mp4 21.3 MB
63 - Appendix - pandas Fundamentals/004 Parameters and Arguments in pandas.mp4 21.1 MB
64 - Appendix - Working with Text Files in Python/024 Saving Your Data with pandas.mp4 21.1 MB
63 - Appendix - pandas Fundamentals/006 Using .sort_values().mp4 21.0 MB
12 - Probability - Distributions/012 Continuous Distributions The Chi-Squared Distribution.mp4 21.0 MB
64 - Appendix - Working with Text Files in Python/027 Saving Your Data with NumPy - Part III - .csv.mp4 20.8 MB
63 - Appendix - pandas Fundamentals/012 pandas DataFrames - Indexing with .loc[].mp4 20.7 MB
55 - Appendix Deep Learning - TensorFlow 1 Business Case/011 Business Case A Comment on the Homework.mp4 20.6 MB
40 - Part 6 Mathematics/008 Transpose of a Matrix.mp4 20.5 MB
34 - Advanced Statistical Methods - Linear Regression with sklearn/010 Feature Selection (F-regression).mp4 20.5 MB
34 - Advanced Statistical Methods - Linear Regression with sklearn/016 Predicting with the Standardized Coefficients.mp4 20.4 MB
34 - Advanced Statistical Methods - Linear Regression with sklearn/014 Feature Scaling (Standardization).mp4 20.4 MB
45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/007 Backpropagation.mp4 20.3 MB
36 - Advanced Statistical Methods - Logistic Regression/012 Calculating the Accuracy of the Model.mp4 20.3 MB
11 - Probability - Bayesian Inference/010 The Multiplication Law.mp4 20.2 MB
29 - Python - Iterations/002 While Loops and Incrementing.mp4 20.2 MB
15 - Statistics - Descriptive Statistics/017 Standard Deviation and Coefficient of Variation.mp4 20.1 MB
58 - Case Study - Preprocessing the 'Absenteeism_data'/030 Analyzing Several Straightforward Columns for this Exercise.mp4 20.1 MB
12 - Probability - Distributions/009 Continuous Distributions The Normal Distribution.mp4 20.0 MB
61 - Case Study - Analyzing the Predicted Outputs in Tableau/006 Analyzing Transportation Expense vs Probability in Tableau.mp4 19.8 MB
58 - Case Study - Preprocessing the 'Absenteeism_data'/032 Final Remarks of this Section.mp4 19.7 MB
15 - Statistics - Descriptive Statistics/009 Cross Tables and Scatter Plots.mp4 19.7 MB
20 - Statistics - Hypothesis Testing/008 Test for the Mean. Population Variance Unknown.mp4 19.7 MB
57 - Case Study - What's Next in the Course/001 Game Plan for this Python, SQL, and Tableau Business Exercise.mp4 19.7 MB
60 - Case Study - Loading the 'absenteeism_module'/002 Deploying the 'absenteeism_module' - Part I.mp4 19.6 MB
64 - Appendix - Working with Text Files in Python/023 Importing Files in Jupyter.mp4 19.6 MB
63 - Appendix - pandas Fundamentals/002 Working with Methods in Python - Part I.mp4 19.6 MB
58 - Case Study - Preprocessing the 'Absenteeism_data'/002 Importing the Absenteeism Data in Python.mp4 19.5 MB
56 - Software Integration/001 What are Data, Servers, Clients, Requests, and Responses.mp4 19.5 MB
12 - Probability - Distributions/001 Fundamentals of Probability Distributions.mp4 19.4 MB
15 - Statistics - Descriptive Statistics/021 Correlation Coefficient.mp4 19.3 MB
11 - Probability - Bayesian Inference/002 Ways Sets Can Interact.mp4 19.3 MB
28 - Python - Sequences/003 List Slicing.mp4 19.2 MB
53 - Appendix Deep Learning - TensorFlow 1 Introduction/009 Basic NN Example with TF Model Output.mp4 19.1 MB
58 - Case Study - Preprocessing the 'Absenteeism_data'/028 Extracting the Day of the Week from the Date Column.mp4 19.1 MB
25 - Python - Other Python Operators/002 Logical and Identity Operators.mp4 19.0 MB
40 - Part 6 Mathematics/004 Arrays in Python - A Convenient Way To Represent Matrices.mp4 19.0 MB
22 - Part 4 Introduction to Python/006 Prerequisites for Coding in the Jupyter Notebooks.mp4 19.0 MB
18 - Statistics - Inferential Statistics Confidence Intervals/004 Confidence Interval Clarifications.mp4 18.9 MB
64 - Appendix - Working with Text Files in Python/025 Saving Your Data with NumPy - Part I - .npy.mp4 18.9 MB
36 - Advanced Statistical Methods - Logistic Regression/006 An Invaluable Coding Tip.mp4 18.8 MB
20 - Statistics - Hypothesis Testing/004 Type I Error and Type II Error.mp4 18.6 MB
55 - Appendix Deep Learning - TensorFlow 1 Business Case/009 Business Case Interpretation.mp4 18.6 MB
15 - Statistics - Descriptive Statistics/019 Covariance.mp4 18.4 MB
05 - The Field of Data Science - Popular Data Science Techniques/002 Real Life Examples of Traditional Data.mp4 18.4 MB
39 - Advanced Statistical Methods - Other Types of Clustering/002 Dendrogram.mp4 18.3 MB
10 - Probability - Combinatorics/005 Solving Variations without Repetition.mp4 18.2 MB
28 - Python - Sequences/004 Tuples.mp4 18.2 MB
58 - Case Study - Preprocessing the 'Absenteeism_data'/004 Introduction to Terms with Multiple Meanings.mp4 18.0 MB
63 - Appendix - pandas Fundamentals/008 Introduction to pandas DataFrames - Part II.mp4 17.8 MB
53 - Appendix Deep Learning - TensorFlow 1 Introduction/007 Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.mp4 17.7 MB
15 - Statistics - Descriptive Statistics/005 Numerical Variables - Frequency Distribution Table.mp4 17.7 MB
11 - Probability - Bayesian Inference/001 Sets and Events.mp4 17.7 MB
10 - Probability - Combinatorics/002 Permutations and How to Use Them.mp4 17.5 MB
58 - Case Study - Preprocessing the 'Absenteeism_data'/023 Creating Checkpoints while Coding in Jupyter.mp4 17.3 MB
17 - Statistics - Inferential Statistics Fundamentals/002 What is a Distribution.mp4 17.2 MB
58 - Case Study - Preprocessing the 'Absenteeism_data'/031 Working on Education, Children, and Pets.mp4 16.9 MB
53 - Appendix Deep Learning - TensorFlow 1 Introduction/004 TensorFlow Intro.mp4 16.9 MB
42 - Deep Learning - Introduction to Neural Networks/012 Optimization Algorithm n-Parameter Gradient Descent.mp4 16.8 MB
64 - Appendix - Working with Text Files in Python/005 Importing Data in Python - Principles.mp4 16.7 MB
44 - Deep Learning - TensorFlow 2.0 Introduction/008 Customizing a TensorFlow 2 Model.mp4 16.7 MB
10 - Probability - Combinatorics/003 Simple Operations with Factorials.mp4 16.7 MB
42 - Deep Learning - Introduction to Neural Networks/006 The Linear model with Multiple Inputs and Multiple Outputs.mp4 16.7 MB
35 - Advanced Statistical Methods - Practical Example Linear Regression/004 Practical Example Linear Regression (Part 3).mp4 16.7 MB
11 - Probability - Bayesian Inference/007 The Conditional Probability Formula.mp4 16.6 MB
29 - Python - Iterations/006 How to Iterate over Dictionaries.mp4 16.4 MB
10 - Probability - Combinatorics/009 Combinatorics in Real-Life The Lottery.mp4 16.4 MB
12 - Probability - Distributions/014 Continuous Distributions The Logistic Distribution.mp4 16.2 MB
52 - Deep Learning - Conclusion/006 An Overview of non-NN Approaches.mp4 16.1 MB
12 - Probability - Distributions/013 Continuous Distributions The Exponential Distribution.mp4 16.0 MB
54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/005 MNIST Loss and Optimization Algorithm.mp4 15.8 MB
53 - Appendix Deep Learning - TensorFlow 1 Introduction/008 Basic NN Example with TF Loss Function and Gradient Descent.mp4 15.7 MB
23 - Python - Variables and Data Types/003 Python Strings.mp4 15.7 MB
43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/003 Basic NN Example (Part 3).mp4 15.6 MB
65 - Bonus Lecture/001 365-Data-Science-Data-Science-Interview-Questions-Guide.pdf 15.6 MB
40 - Part 6 Mathematics/005 What is a Tensor.mp4 15.5 MB
20 - Statistics - Hypothesis Testing/012 Test for the mean. Independent Samples (Part 1).mp4 15.4 MB
44 - Deep Learning - TensorFlow 2.0 Introduction/003 TensorFlow 1 vs TensorFlow 2.mp4 15.3 MB
44 - Deep Learning - TensorFlow 2.0 Introduction/002 TensorFlow Outline and Comparison with Other Libraries.mp4 15.3 MB
43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/002 Basic NN Example (Part 2).mp4 15.2 MB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/004 Standardizing the Data.mp4 15.2 MB
12 - Probability - Distributions/005 Discrete Distributions The Bernoulli Distribution.mp4 15.1 MB
10 - Probability - Combinatorics/010 A Recap of Combinatorics.mp4 15.0 MB
11 - Probability - Bayesian Inference/006 Dependence and Independence of Sets.mp4 14.9 MB
22 - Part 4 Introduction to Python/001 Introduction to Programming.mp4 14.8 MB
18 - Statistics - Inferential Statistics Confidence Intervals/013 Confidence intervals. Two means. Independent Samples (Part 2).mp4 14.6 MB
36 - Advanced Statistical Methods - Logistic Regression/007 Understanding Logistic Regression Tables.mp4 14.6 MB
37 - Advanced Statistical Methods - Cluster Analysis/001 Introduction to Cluster Analysis.mp4 14.5 MB
64 - Appendix - Working with Text Files in Python/020 Working with Excel (.xlsx) Data.mp4 14.4 MB
26 - Python - Conditional Statements/003 The ELIF Statement.mp4 14.2 MB
46 - Deep Learning - Overfitting/002 Underfitting and Overfitting for Classification.mp4 14.0 MB
10 - Probability - Combinatorics/004 Solving Variations with Repetition.mp4 13.9 MB
51 - Deep Learning - Business Case Example/006 Business Case Load the Preprocessed Data.mp4 13.8 MB
07 - The Field of Data Science - Careers in Data Science/001 Finding the Job - What to Expect and What to Look for.mp4 13.8 MB
10 - Probability - Combinatorics/007 Symmetry of Combinations.mp4 13.8 MB
58 - Case Study - Preprocessing the 'Absenteeism_data'/006 Using a Statistical Approach towards the Solution to the Exercise.mp4 13.7 MB
40 - Part 6 Mathematics/003 Linear Algebra and Geometry.mp4 13.7 MB
23 - Python - Variables and Data Types/002 Numbers and Boolean Values in Python.mp4 13.7 MB
18 - Statistics - Inferential Statistics Confidence Intervals/006 Confidence Intervals; Population Variance Unknown; T-score.mp4 13.7 MB
18 - Statistics - Inferential Statistics Confidence Intervals/005 Student's T Distribution.mp4 13.7 MB
17 - Statistics - Inferential Statistics Fundamentals/007 Standard error.mp4 13.5 MB
32 - Advanced Statistical Methods - Linear Regression with StatsModels/001 The Linear Regression Model.mp4 13.5 MB
52 - Deep Learning - Conclusion/004 An overview of CNNs.mp4 13.4 MB
15 - Statistics - Descriptive Statistics/013 Skewness.mp4 13.3 MB
64 - Appendix - Working with Text Files in Python/006 Plain Text Files, Flat Files and More.mp4 13.2 MB
05 - The Field of Data Science - Popular Data Science Techniques/004 Real Life Examples of Big Data.mp4 13.1 MB
42 - Deep Learning - Introduction to Neural Networks/003 Types of Machine Learning.mp4 13.1 MB
47 - Deep Learning - Initialization/001 What is Initialization.mp4 12.9 MB
40 - Part 6 Mathematics/009 Dot Product.mp4 12.8 MB
62 - Appendix - Additional Python Tools/002 Iterating Over Range Objects.mp4 12.6 MB
50 - Deep Learning - Classifying on the MNIST Dataset/003 MNIST Importing the Relevant Packages and Loading the Data.mp4 12.2 MB
62 - Appendix - Additional Python Tools/003 Introduction to Nested For Loops.mp4 12.1 MB
49 - Deep Learning - Preprocessing/003 Standardization.mp4 12.1 MB
22 - Part 4 Introduction to Python/002 Why Python.mp4 12.0 MB
64 - Appendix - Working with Text Files in Python/001 An Introduction to Working with Files in Python.mp4 12.0 MB
18 - Statistics - Inferential Statistics Confidence Intervals/011 Confidence intervals. Two means. Independent Samples (Part 1).mp4 12.0 MB
29 - Python - Iterations/003 Lists with the range() Function.mp4 11.9 MB
40 - Part 6 Mathematics/001 What is a Matrix.mp4 11.9 MB
41 - Part 7 Deep Learning/001 What to Expect from this Part.mp4 11.7 MB
64 - Appendix - Working with Text Files in Python/014 Importing Data with index_col.mp4 11.6 MB
11 - Probability - Bayesian Inference/008 The Law of Total Probability.mp4 11.6 MB
36 - Advanced Statistical Methods - Logistic Regression/009 What do the Odds Actually Mean.mp4 11.4 MB
54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/003 MNIST Relevant Packages.mp4 11.3 MB
32 - Advanced Statistical Methods - Linear Regression with StatsModels/011 R-Squared.mp4 11.2 MB
02 - The Field of Data Science - The Various Data Science Disciplines/002 What is the difference between Analysis and Analytics.mp4 11.2 MB
38 - Advanced Statistical Methods - K-Means Clustering/008 Pros and Cons of K-Means Clustering.mp4 11.1 MB
11 - Probability - Bayesian Inference/009 The Additive Rule.mp4 11.1 MB
64 - Appendix - Working with Text Files in Python/003 Structured, Semi-Structured and Unstructured Data.mp4 11.1 MB
11 - Probability - Bayesian Inference/003 Intersection of Sets.mp4 11.0 MB
34 - Advanced Statistical Methods - Linear Regression with sklearn/007 Multiple Linear Regression with sklearn.mp4 11.0 MB
64 - Appendix - Working with Text Files in Python/012 Importing .csv Files - Part II.mp4 10.9 MB
38 - Advanced Statistical Methods - K-Means Clustering/009 To Standardize or not to Standardize.mp4 10.9 MB
38 - Advanced Statistical Methods - K-Means Clustering/001 K-Means Clustering.mp4 10.8 MB
64 - Appendix - Working with Text Files in Python/004 Text Files and Data Connectivity.mp4 10.8 MB
46 - Deep Learning - Overfitting/001 What is Overfitting.mp4 10.8 MB
10 - Probability - Combinatorics/008 Solving Combinations with Separate Sample Spaces.mp4 10.6 MB
50 - Deep Learning - Classifying on the MNIST Dataset/009 MNIST Select the Loss and the Optimizer.mp4 10.6 MB
63 - Appendix - pandas Fundamentals/007 Introduction to pandas DataFrames - Part I.mp4 10.6 MB
11 - Probability - Bayesian Inference/005 Mutually Exclusive Sets.mp4 10.6 MB
42 - Deep Learning - Introduction to Neural Networks/001 Introduction to Neural Networks.mp4 10.5 MB
38 - Advanced Statistical Methods - K-Means Clustering/004 Clustering Categorical Data.mp4 10.4 MB
12 - Probability - Distributions/004 Discrete Distributions The Uniform Distribution.mp4 10.3 MB
46 - Deep Learning - Overfitting/006 Early Stopping or When to Stop Training.mp4 10.3 MB
27 - Python - Python Functions/007 Built-in Functions in Python.mp4 10.2 MB
58 - Case Study - Preprocessing the 'Absenteeism_data'/020 Reordering Columns in a Pandas DataFrame in Python.mp4 10.0 MB
52 - Deep Learning - Conclusion/001 Summary on What You've Learned.mp4 9.9 MB
42 - Deep Learning - Introduction to Neural Networks/010 Common Objective Functions Cross-Entropy Loss.mp4 9.8 MB
48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/001 Stochastic Gradient Descent.mp4 9.7 MB
37 - Advanced Statistical Methods - Cluster Analysis/003 Difference between Classification and Clustering.mp4 9.7 MB
15 - Statistics - Descriptive Statistics/007 The Histogram.mp4 9.6 MB
54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/007 MNIST Batching and Early Stopping.mp4 9.5 MB
12 - Probability - Distributions/003 Characteristics of Discrete Distributions.mp4 9.4 MB
64 - Appendix - Working with Text Files in Python/002 File vs File Object, Reading vs Parsing Data.mp4 9.4 MB
46 - Deep Learning - Overfitting/004 Training, Validation, and Test Datasets.mp4 9.4 MB
43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/001 Basic NN Example (Part 1).mp4 9.3 MB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/007 A2 No Endogeneity.mp4 9.2 MB
12 - Probability - Distributions/011 Continuous Distributions The Students' T Distribution.mp4 9.2 MB
49 - Deep Learning - Preprocessing/001 Preprocessing Introduction.mp4 9.2 MB
53 - Appendix Deep Learning - TensorFlow 1 Introduction/005 Actual Introduction to TensorFlow.mp4 9.1 MB
45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/002 What is a Deep Net.mp4 9.1 MB
39 - Advanced Statistical Methods - Other Types of Clustering/001 Types of Clustering.mp4 9.0 MB
63 - Appendix - pandas Fundamentals/003 Working with Methods in Python - Part II.mp4 9.0 MB
23 - Python - Variables and Data Types/001 Variables.mp4 8.9 MB
53 - Appendix Deep Learning - TensorFlow 1 Introduction/006 Types of File Formats, supporting Tensors.mp4 8.9 MB
44 - Deep Learning - TensorFlow 2.0 Introduction/005 Types of File Formats Supporting TensorFlow.mp4 8.9 MB
45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/005 Activation Functions.mp4 8.8 MB
32 - Advanced Statistical Methods - Linear Regression with StatsModels/009 Decomposition of Variability.mp4 8.8 MB
45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/006 Activation Functions Softmax Activation.mp4 8.7 MB
30 - Python - Advanced Python Tools/001 Object Oriented Programming.mp4 8.7 MB
12 - Probability - Distributions/015 FIFA19-post.csv 8.6 MB
12 - Probability - Distributions/015 FIFA19.csv 8.6 MB
24 - Python - Basic Python Syntax/001 Using Arithmetic Operators in Python.mp4 8.6 MB
17 - Statistics - Inferential Statistics Fundamentals/004 The Standard Normal Distribution.mp4 8.6 MB
36 - Advanced Statistical Methods - Logistic Regression/004 Building a Logistic Regression.mp4 8.6 MB
30 - Python - Advanced Python Tools/004 Importing Modules in Python.mp4 8.5 MB
40 - Part 6 Mathematics/002 Scalars and Vectors.mp4 8.5 MB
46 - Deep Learning - Overfitting/003 What is Validation.mp4 8.4 MB
57 - Case Study - What's Next in the Course/002 The Business Task.mp4 8.4 MB
27 - Python - Python Functions/002 How to Create a Function with a Parameter.mp4 8.3 MB
64 - Appendix - Working with Text Files in Python/008 Common Naming Conventions.mp4 8.2 MB
51 - Deep Learning - Business Case Example/011 Business Case Testing the Model.mp4 8.2 MB
22 - Part 4 Introduction to Python/003 Why Jupyter.mp4 8.1 MB
45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/008 Backpropagation Picture.mp4 8.1 MB
48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/005 Learning Rate Schedules Visualized.mp4 8.0 MB
42 - Deep Learning - Introduction to Neural Networks/004 The Linear Model (Linear Algebraic Version).mp4 8.0 MB
50 - Deep Learning - Classifying on the MNIST Dataset/002 MNIST How to Tackle the MNIST.mp4 7.9 MB
42 - Deep Learning - Introduction to Neural Networks/005 The Linear Model with Multiple Inputs.mp4 7.9 MB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/009 A4 No Autocorrelation.mp4 7.9 MB
42 - Deep Learning - Introduction to Neural Networks/007 Graphical Representation of Simple Neural Networks.mp4 7.8 MB
42 - Deep Learning - Introduction to Neural Networks/002 Training the Model.mp4 7.7 MB
58 - Case Study - Preprocessing the 'Absenteeism_data'/029 Absenteeism-Exercise-Preprocessing-LECTURES.ipynb 7.6 MB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/010 A5 No Multicollinearity.mp4 7.6 MB
36 - Advanced Statistical Methods - Logistic Regression/014 Underfitting and Overfitting.mp4 7.5 MB
32 - Advanced Statistical Methods - Linear Regression with StatsModels/007 Using Seaborn for Graphs.mp4 7.4 MB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/004 Test for Significance of the Model (F-Test).mp4 7.2 MB
48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/007 Adam (Adaptive Moment Estimation).mp4 7.1 MB
52 - Deep Learning - Conclusion/005 An Overview of RNNs.mp4 7.0 MB
02 - The Field of Data Science - The Various Data Science Disciplines/004 365-DataScience.png 6.9 MB
02 - The Field of Data Science - The Various Data Science Disciplines/005 365-DataScience.png 6.9 MB
18 - Statistics - Inferential Statistics Confidence Intervals/015 Confidence intervals. Two means. Independent Samples (Part 3).mp4 6.9 MB
54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/002 MNIST How to Tackle the MNIST.mp4 6.5 MB
27 - Python - Python Functions/003 Defining a Function in Python - Part II.mp4 6.5 MB
34 - Advanced Statistical Methods - Linear Regression with sklearn/012 Creating a Summary Table with P-values.mp4 6.4 MB
46 - Deep Learning - Overfitting/005 N-Fold Cross Validation.mp4 6.2 MB
42 - Deep Learning - Introduction to Neural Networks/008 What is the Objective Function.mp4 6.2 MB
26 - Python - Conditional Statements/001 The IF Statement.mp4 6.1 MB
22 - Part 4 Introduction to Python/005 Understanding Jupyter's Interface - the Notebook Dashboard.mp4 6.1 MB
26 - Python - Conditional Statements/002 The ELSE Statement.mp4 6.0 MB
27 - Python - Python Functions/005 Conditional Statements and Functions.mp4 6.0 MB
10 - Probability - Combinatorics/001 Fundamentals of Combinatorics.mp4 5.9 MB
36 - Advanced Statistical Methods - Logistic Regression/001 Introduction to Logistic Regression.mp4 5.9 MB
34 - Advanced Statistical Methods - Linear Regression with sklearn/018 Underfitting and Overfitting.mp4 5.8 MB
58 - Case Study - Preprocessing the 'Absenteeism_data'/015 More on Dummy Variables A Statistical Perspective.mp4 5.8 MB
40 - Part 6 Mathematics/007 Errors when Adding Matrices.mp4 5.8 MB
47 - Deep Learning - Initialization/002 Types of Simple Initializations.mp4 5.7 MB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/001 Multiple Linear Regression.mp4 5.7 MB
42 - Deep Learning - Introduction to Neural Networks/009 Common Objective Functions L2-norm Loss.mp4 5.5 MB
47 - Deep Learning - Initialization/003 State-of-the-Art Method - (Xavier) Glorot Initialization.mp4 5.5 MB
49 - Deep Learning - Preprocessing/004 Preprocessing Categorical Data.mp4 5.4 MB
34 - Advanced Statistical Methods - Linear Regression with sklearn/002 How are we Going to Approach this Section.mp4 5.3 MB
37 - Advanced Statistical Methods - Cluster Analysis/004 Math Prerequisites.mp4 5.3 MB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/005 OLS Assumptions.mp4 5.3 MB
48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/003 Momentum.mp4 5.2 MB
45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/001 What is a Layer.mp4 5.2 MB
30 - Python - Advanced Python Tools/003 What is the Standard Library.mp4 5.0 MB
53 - Appendix Deep Learning - TensorFlow 1 Introduction/002 How to Install TensorFlow 1.mp4 5.0 MB
64 - Appendix - Working with Text Files in Python/007 Text Files of Fixed Width.mp4 4.8 MB
25 - Python - Other Python Operators/001 Comparison Operators.mp4 4.8 MB
54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/001 MNIST What is the MNIST Dataset.mp4 4.8 MB
52 - Deep Learning - Conclusion/002 What's Further out there in terms of Machine Learning.mp4 4.8 MB
44 - Deep Learning - TensorFlow 2.0 Introduction/004 A Note on TensorFlow 2 Syntax.mp4 4.6 MB
50 - Deep Learning - Classifying on the MNIST Dataset/001 MNIST The Dataset.mp4 4.5 MB
55 - Appendix Deep Learning - TensorFlow 1 Business Case/010 Business Case Testing the Model.mp4 4.3 MB
29 - Python - Iterations/005 Conditional Statements, Functions, and Loops.mp4 4.3 MB
26 - Python - Conditional Statements/004 A Note on Boolean Values.mp4 4.2 MB
55 - Appendix Deep Learning - TensorFlow 1 Business Case/002 Business Case Outlining the Solution.mp4 4.2 MB
32 - Advanced Statistical Methods - Linear Regression with StatsModels/002 Correlation vs Regression.mp4 3.8 MB
48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/002 Problems with Gradient Descent.mp4 3.7 MB
31 - Part 5 Advanced Statistical Methods in Python/001 Introduction to Regression Analysis.mp4 3.6 MB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/006 A1 Linearity.mp4 3.6 MB
38 - Advanced Statistical Methods - K-Means Clustering/010 Relationship between Clustering and Regression.mp4 3.5 MB
49 - Deep Learning - Preprocessing/002 Types of Basic Preprocessing.mp4 3.2 MB
27 - Python - Python Functions/001 Defining a Function in Python.mp4 3.2 MB
27 - Python - Python Functions/004 How to Use a Function within a Function.mp4 3.2 MB
17 - Statistics - Inferential Statistics Fundamentals/001 Introduction.mp4 3.1 MB
51 - Deep Learning - Business Case Example/002 Business Case Outlining the Solution.mp4 3.0 MB
27 - Python - Python Functions/006 Functions Containing a Few Arguments.mp4 2.8 MB
24 - Python - Basic Python Syntax/007 Structuring with Indentation.mp4 2.8 MB
24 - Python - Basic Python Syntax/002 The Double Equality Sign.mp4 2.7 MB
24 - Python - Basic Python Syntax/004 Add Comments.mp4 2.4 MB
24 - Python - Basic Python Syntax/006 Indexing Elements.mp4 2.4 MB
22 - Part 4 Introduction to Python/001 Introduction-to-Python-Course-Notes.pdf 2.2 MB
23 - Python - Variables and Data Types/001 Introduction-to-Python-Course-Notes.pdf 2.2 MB
64 - Appendix - Working with Text Files in Python/029 Working with Text Files in Python - Conclusion.mp4 2.1 MB
30 - Python - Advanced Python Tools/002 Modules and Packages.mp4 2.1 MB
24 - Python - Basic Python Syntax/003 How to Reassign Values.mp4 1.9 MB
19 - Statistics - Practical Example Inferential Statistics/002 3.17.Practical-example.Confidence-intervals-exercise-solution.xlsx 1.8 MB
19 - Statistics - Practical Example Inferential Statistics/001 3.17.Practical-example.Confidence-intervals-lesson.xlsx 1.7 MB
19 - Statistics - Practical Example Inferential Statistics/002 3.17.Practical-example.Confidence-intervals-exercise.xlsx 1.7 MB
24 - Python - Basic Python Syntax/005 Understanding Line Continuation.mp4 1.2 MB
20 - Statistics - Hypothesis Testing/007 Online-p-value-calculator.pdf 1.2 MB
45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/001 Course-Notes-Section-6.pdf 936.4 KB
45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/002 Course-Notes-Section-6.pdf 936.4 KB
11 - Probability - Bayesian Inference/012 CDS-2017-2018-Hamilton.pdf 845.3 KB
35 - Advanced Statistical Methods - Practical Example Linear Regression/008 sklearn-Linear-Regression-Practical-Example-Part-5-with-comments.ipynb 711.0 KB
51 - Deep Learning - Business Case Example/001 Audiobooks-data.csv 710.8 KB
55 - Appendix Deep Learning - TensorFlow 1 Business Case/001 Audiobooks-data.csv 710.8 KB
55 - Appendix Deep Learning - TensorFlow 1 Business Case/003 Audiobooks-data.csv 710.8 KB
55 - Appendix Deep Learning - TensorFlow 1 Business Case/004 Audiobooks-data.csv 710.8 KB
55 - Appendix Deep Learning - TensorFlow 1 Business Case/005 Audiobooks-data.csv 710.8 KB
55 - Appendix Deep Learning - TensorFlow 1 Business Case/011 Audiobooks-data.csv 710.8 KB
55 - Appendix Deep Learning - TensorFlow 1 Business Case/012 Audiobooks-data.csv 710.8 KB
35 - Advanced Statistical Methods - Practical Example Linear Regression/008 sklearn-Linear-Regression-Practical-Example-Part-5.ipynb 698.4 KB
20 - Statistics - Hypothesis Testing/001 Course-notes-hypothesis-testing.pdf 656.4 KB
20 - Statistics - Hypothesis Testing/003 Course-notes-hypothesis-testing.pdf 656.4 KB
64 - Appendix - Working with Text Files in Python/001 Common-Naming-Conventions.pdf 643.8 KB
64 - Appendix - Working with Text Files in Python/008 Common-Naming-Conventions.pdf 643.8 KB
43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/001 Shortcuts-for-Jupyter.pdf 619.2 KB
44 - Deep Learning - TensorFlow 2.0 Introduction/001 Shortcuts-for-Jupyter.pdf 619.2 KB
53 - Appendix Deep Learning - TensorFlow 1 Introduction/005 Shortcuts-for-Jupyter.pdf 619.2 KB
42 - Deep Learning - Introduction to Neural Networks/001 Course-Notes-Section-2.pdf 578.1 KB
42 - Deep Learning - Introduction to Neural Networks/002 Course-Notes-Section-2.pdf 578.1 KB
14 - Part 3 Statistics/001 Course-notes-descriptive-statistics.pdf 482.2 KB
15 - Statistics - Descriptive Statistics/001 Course-notes-descriptive-statistics.pdf 482.2 KB
12 - Probability - Distributions/001 Course-Notes-Probability-Distributions.pdf 463.9 KB
35 - Advanced Statistical Methods - Practical Example Linear Regression/006 sklearn-Linear-Regression-Practical-Example-Part-4-with-comments.ipynb 407.6 KB
35 - Advanced Statistical Methods - Practical Example Linear Regression/006 sklearn-Linear-Regression-Practical-Example-Part-4.ipynb 397.2 KB
11 - Probability - Bayesian Inference/001 Course-Notes-Bayesian-Inference.pdf 386.0 KB
17 - Statistics - Inferential Statistics Fundamentals/001 Course-notes-inferential-statistics.pdf 382.3 KB
17 - Statistics - Inferential Statistics Fundamentals/002 Course-notes-inferential-statistics.pdf 382.3 KB
09 - Part 2 Probability/001 Course-Notes-Basic-Probability.pdf 371.1 KB
35 - Advanced Statistical Methods - Practical Example Linear Regression/005 sklearn-Dummies-and-VIF-Exercise-Solution.ipynb 370.2 KB
35 - Advanced Statistical Methods - Practical Example Linear Regression/004 sklearn-Linear-Regression-Practical-Example-Part-3-with-comments.ipynb 351.5 KB
35 - Advanced Statistical Methods - Practical Example Linear Regression/005 sklearn-Dummies-and-VIF-Exercise.ipynb 344.6 KB
12 - Probability - Distributions/008 Solving-Integrals.pdf 343.9 KB
35 - Advanced Statistical Methods - Practical Example Linear Regression/004 sklearn-Linear-Regression-Practical-Example-Part-3.ipynb 343.6 KB
35 - Advanced Statistical Methods - Practical Example Linear Regression/002 sklearn-Linear-Regression-Practical-Example-Part-2-with-comments.ipynb 335.6 KB
36 - Advanced Statistical Methods - Logistic Regression/001 Course-Notes-Logistic-Regression.pdf 335.2 KB
36 - Advanced Statistical Methods - Logistic Regression/002 Course-Notes-Logistic-Regression.pdf 335.2 KB
35 - Advanced Statistical Methods - Practical Example Linear Regression/002 sklearn-Linear-Regression-Practical-Example-Part-2.ipynb 328.7 KB
02 - The Field of Data Science - The Various Data Science Disciplines/003 365-DataScience-Diagram.pdf 323.1 KB
02 - The Field of Data Science - The Various Data Science Disciplines/004 365-DataScience-Diagram.pdf 323.1 KB
13 - Probability - Probability in Other Fields/003 Probability-Cheat-Sheet.pdf 320.3 KB
31 - Part 5 Advanced Statistical Methods in Python/001 Course-notes-regression-analysis.pdf 312.2 KB
32 - Advanced Statistical Methods - Linear Regression with StatsModels/001 Course-notes-regression-analysis.pdf 312.2 KB
01 - Part 1 Introduction/003 FAQ-The-Data-Science-Course.pdf 306.1 KB
15 - Statistics - Descriptive Statistics/004 Statistics-PDF-with-Excel-Solutions-that-dont-visualize-properly.pdf 289.1 KB
15 - Statistics - Descriptive Statistics/008 Statistics-PDF-with-Excel-Solutions-that-dont-visualize-properly.pdf 289.1 KB
10 - Probability - Combinatorics/011 Additional-Exercises-Combinatorics-Solutions.pdf 245.7 KB
10 - Probability - Combinatorics/001 Course-Notes-Combinatorics.pdf 226.1 KB
35 - Advanced Statistical Methods - Practical Example Linear Regression/001 1.04.Real-life-example.csv 219.8 KB
35 - Advanced Statistical Methods - Practical Example Linear Regression/002 1.04.Real-life-example.csv 219.8 KB
35 - Advanced Statistical Methods - Practical Example Linear Regression/005 1.04.Real-life-example.csv 219.8 KB
35 - Advanced Statistical Methods - Practical Example Linear Regression/006 1.04.Real-life-example.csv 219.8 KB
35 - Advanced Statistical Methods - Practical Example Linear Regression/008 1.04.Real-life-example.csv 219.8 KB
64 - Appendix - Working with Text Files in Python/018 Lending-company.json 213.5 KB
37 - Advanced Statistical Methods - Cluster Analysis/001 Course-Notes-Cluster-Analysis.pdf 208.7 KB
37 - Advanced Statistical Methods - Cluster Analysis/002 Course-Notes-Cluster-Analysis.pdf 208.7 KB
10 - Probability - Combinatorics/006 Combinations-With-Repetition.pdf 207.4 KB
13 - Probability - Probability in Other Fields/001 Probability-in-Finance-Solutions.pdf 184.5 KB
45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/009 Backpropagation-a-peek-into-the-Mathematics-of-Optimization.pdf 182.4 KB
35 - Advanced Statistical Methods - Practical Example Linear Regression/001 sklearn-Linear-Regression-Practical-Example-Part-1-with-comments.ipynb 171.4 KB
35 - Advanced Statistical Methods - Practical Example Linear Regression/001 sklearn-Linear-Regression-Practical-Example-Part-1.ipynb 166.9 KB
63 - Appendix - pandas Fundamentals/001 Sales-products.csv 152.3 KB
63 - Appendix - pandas Fundamentals/012 Sales-products.csv 152.3 KB
16 - Statistics - Practical Example Descriptive Statistics/001 2.13.Practical-example.Descriptive-statistics-lesson.xlsx 146.5 KB
16 - Statistics - Practical Example Descriptive Statistics/002 2.13.Practical-example.Descriptive-statistics-exercise-solution.xlsx 146.4 KB
12 - Probability - Distributions/007 Poisson-Expected-Value-and-Variance.pdf 146.0 KB
12 - Probability - Distributions/009 Normal-Distribution-Exp-and-Var.pdf 144.1 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/001 data-preprocessing-homework.pdf 134.5 KB
16 - Statistics - Practical Example Descriptive Statistics/002 2.13.Practical-example.Descriptive-statistics-exercise.xlsx 120.3 KB
63 - Appendix - pandas Fundamentals/001 pandas-Fundamentals-Solutions.ipynb 118.3 KB
63 - Appendix - pandas Fundamentals/012 pandas-Fundamentals-Solutions.ipynb 118.3 KB
64 - Appendix - Working with Text Files in Python/011 Lending-company-single-column-data.csv 114.5 KB
63 - Appendix - pandas Fundamentals/001 Lending-company.csv 112.4 KB
63 - Appendix - pandas Fundamentals/012 Lending-company.csv 112.4 KB
64 - Appendix - Working with Text Files in Python/011 Lending-company.csv 112.4 KB
36 - Advanced Statistical Methods - Logistic Regression/016 Testing-the-Model-Solution.ipynb 111.1 KB
13 - Probability - Probability in Other Fields/001 Probability-in-Finance-Homework.pdf 110.7 KB
10 - Probability - Combinatorics/011 Additional-Exercises-Combinatorics.pdf 106.6 KB
64 - Appendix - Working with Text Files in Python/020 Lending-company.xlsx 93.1 KB
10 - Probability - Combinatorics/007 Symmetry-Explained.pdf 85.0 KB
44 - Deep Learning - TensorFlow 2.0 Introduction/009 TensorFlow-Minimal-Example-Exercise-3-Solution.ipynb 84.4 KB
43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/005 Minimal-example-Exercise-3.d.Solution.ipynb 84.1 KB
44 - Deep Learning - TensorFlow 2.0 Introduction/009 TensorFlow-Minimal-Example-Exercise-2-1-Solution.ipynb 83.7 KB
44 - Deep Learning - TensorFlow 2.0 Introduction/009 TensorFlow-Minimal-example-All-exercises.ipynb 83.6 KB
44 - Deep Learning - TensorFlow 2.0 Introduction/008 TensorFlow-Minimal-example-complete-with-comments.ipynb 82.3 KB
36 - Advanced Statistical Methods - Logistic Regression/013 Calculating-the-Accuracy-of-the-Model-Solution.ipynb 81.2 KB
44 - Deep Learning - TensorFlow 2.0 Introduction/009 TensorFlow-Minimal-Example-Exercise-2-2-Solution.ipynb 77.5 KB
44 - Deep Learning - TensorFlow 2.0 Introduction/008 TensorFlow-Minimal-example-complete.ipynb 76.9 KB
44 - Deep Learning - TensorFlow 2.0 Introduction/007 TensorFlow-Minimal-example-Part3.ipynb 76.5 KB
43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/005 Minimal-example-Exercise-3.c.Solution.ipynb 70.1 KB
43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/005 Minimal-example-Exercise-1-Solution.ipynb 69.0 KB
43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/005 Minimal-example-Exercise-5-Solution.ipynb 68.9 KB
43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/005 Minimal-example-Exercise-3.a.Solution.ipynb 67.9 KB
43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/005 Minimal-example-Exercise-3.b.Solution.ipynb 67.7 KB
43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/005 Minimal-example-Exercise-4-Solution.ipynb 66.5 KB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/temp/index_15251170.mpd 65.3 KB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/temp/index_15251152.mpd 63.8 KB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/temp/index_15185936.mpd 63.3 KB
60 - Case Study - Loading the 'absenteeism_module'/001 Absenteeism-Exercise-Integration.ipynb 62.4 KB
43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/005 Minimal-example-Exercise-6-Solution.ipynb 61.8 KB
43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/005 Minimal-example-Exercise-6.ipynb 61.8 KB
43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/005 Minimal-example-Exercise-2-Solution.ipynb 61.4 KB
06 - The Field of Data Science - Popular Data Science Tools/001 Necessary Programming Languages and Software Used in Data Science.encrypted.m4a.part.frag.urls 61.0 KB
06 - The Field of Data Science - Popular Data Science Tools/001 Necessary Programming Languages and Software Used in Data Science.encrypted.mp4.part.frag.urls 61.0 KB
03 - The Field of Data Science - Connecting the Data Science Disciplines/temp/index_13908866.mpd 59.3 KB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/temp/index_15256016.mpd 59.3 KB
32 - Advanced Statistical Methods - Linear Regression with StatsModels/temp/index_13060600.mpd 58.9 KB
64 - Appendix - Working with Text Files in Python/025 Lending-Company-Saving.csv 58.4 KB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/temp/index_15293084.mpd 56.8 KB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/temp/index_15251158.mpd 56.4 KB
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59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/temp/index_15251164.mpd 54.7 KB
21 - Statistics - Practical Example Hypothesis Testing/001 4.10.Hypothesis-testing-section-practical-example.xlsx 51.9 KB
53 - Appendix Deep Learning - TensorFlow 1 Introduction/010 TensorFlow-Minimal-Example-Exercise-2-3-Solution.ipynb 50.0 KB
21 - Statistics - Practical Example Hypothesis Testing/002 4.10.Hypothesis-testing-section-practical-example-exercise-solution.xlsx 44.3 KB
21 - Statistics - Practical Example Hypothesis Testing/002 4.10.Hypothesis-testing-section-practical-example-exercise.xlsx 43.7 KB
42 - Deep Learning - Introduction to Neural Networks/011 GD-function-example.xlsx 42.3 KB
06 - The Field of Data Science - Popular Data Science Tools/temp/index_13040208.mpd 41.1 KB
15 - Statistics - Descriptive Statistics/004 2.3.Categorical-variables.Visualization-techniques-exercise-solution.xlsx 41.1 KB
15 - Statistics - Descriptive Statistics/010 2.6.Cross-table-and-scatter-plot-exercise-solution.xlsx 40.4 KB
48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/temp/index_13061100.mpd 39.2 KB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/temp/index_15251168.mpd 39.2 KB
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33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/temp/index_17572166.mpd 37.8 KB
48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/temp/index_13061110.mpd 35.9 KB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/013 Making Predictions with the Linear Regression.encrypted.mp4.part.frag.urls 35.9 KB
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15 - Statistics - Descriptive Statistics/013 2.8.Skewness-lesson.xlsx 34.6 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/001 Absenteeism-data.csv 32.0 KB
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34 - Advanced Statistical Methods - Linear Regression with sklearn/016 sklearn-Making-Predictions-with-the-Standardized-Coefficients.ipynb 29.8 KB
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59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/001 Absenteeism-preprocessed.csv 29.1 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/001 df-preprocessed.csv 29.1 KB
64 - Appendix - Working with Text Files in Python/015 Lending-Company-Numeric-Data-NAN.csv 28.6 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/004 sklearn-Simple-Linear-Regression-with-comments.ipynb 28.4 KB
44 - Deep Learning - TensorFlow 2.0 Introduction/009 TensorFlow-Minimal-example-Exercise-1-Solution.ipynb 28.0 KB
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11 - Probability - Bayesian Inference/012 Bayesian-Homework.pdf 27.3 KB
53 - Appendix Deep Learning - TensorFlow 1 Introduction/010 TensorFlow-Minimal-Example-Exercise-4-Solution.ipynb 27.0 KB
53 - Appendix Deep Learning - TensorFlow 1 Introduction/010 TensorFlow-Minimal-Example-Exercise-3-Solution.ipynb 26.7 KB
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62 - Appendix - Additional Python Tools/001 Additional-Python-Tools-Solutions.ipynb 25.5 KB
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16 - Statistics - Practical Example Descriptive Statistics/001 Practical Example Descriptive Statistics_en.srt 25.4 KB
15 - Statistics - Descriptive Statistics/019 2.11.Covariance-lesson.xlsx 24.9 KB
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11 - Probability - Bayesian Inference/012 A Practical Example of Bayesian Inference_en.srt 24.3 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/001 What is sklearn and How is it Different from Other Packages.encrypted.m4a.part.frag.urls 24.2 KB
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17 - Statistics - Inferential Statistics Fundamentals/005 3.4.Standard-normal-distribution-exercise-solution.xlsx 24.0 KB
53 - Appendix Deep Learning - TensorFlow 1 Introduction/010 TensorFlow-Minimal-Example-Exercise-1-Solution.ipynb 23.6 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/016 sklearn-Making-Predictions-with-the-Standardized-Coefficients-with-comments.ipynb 22.0 KB
53 - Appendix Deep Learning - TensorFlow 1 Introduction/010 TensorFlow-Minimal-Example-Exercise-2-4-Solution.ipynb 21.7 KB
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64 - Appendix - Working with Text Files in Python/015 Importing Data with .loadtxt() and .genfromtxt()_en.srt 17.8 KB
39 - Advanced Statistical Methods - Other Types of Clustering/003 Heatmaps-with-comments.ipynb 17.7 KB
54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/011 TensorFlow-MNIST-around-98-percent-accuracy.ipynb 17.7 KB
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12 - Probability - Distributions/015 Customers-Membership-post.xlsx 15.6 KB
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50 - Deep Learning - Classifying on the MNIST Dataset/011 TensorFlow-MNIST-around-98-percent-accuracy.ipynb 15.0 KB
02 - The Field of Data Science - The Various Data Science Disciplines/004 Continuing with BI, ML, and AI_en.srt 15.0 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/015 sklearn-Feature-Selection-through-Feature-Scaling-Standardization-Part-2.ipynb 14.9 KB
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50 - Deep Learning - Classifying on the MNIST Dataset/011 1.TensorFlow-MNIST-Width-Solution.ipynb 14.8 KB
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20 - Statistics - Hypothesis Testing/011 4.7.Test-for-the-mean.Dependent-samples-exercise-solution.xlsx 14.4 KB
55 - Appendix Deep Learning - TensorFlow 1 Business Case/011 TensorFlow-Audiobooks-Machine-learning-Homework.ipynb 14.4 KB
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54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/011 4.TensorFlow-MNIST-Activation-functions-Part-1-Solution.ipynb 14.3 KB
35 - Advanced Statistical Methods - Practical Example Linear Regression/006 Practical Example Linear Regression (Part 4)_en.srt 14.3 KB
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18 - Statistics - Inferential Statistics Confidence Intervals/010 3.13.Confidence-intervals.Two-means.Dependent-samples-exercise-solution.xlsx 14.2 KB
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53 - Appendix Deep Learning - TensorFlow 1 Introduction/010 TensorFlow-Minimal-Example-All-Exercises.ipynb 14.0 KB
54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/011 5.TensorFlow-MNIST-Activation-functions-Part-2-Solution.ipynb 13.9 KB
05 - The Field of Data Science - Popular Data Science Techniques/007 Techniques for Working with Traditional Methods_en.srt 13.9 KB
40 - Part 6 Mathematics/011 Why is Linear Algebra Useful_en.srt 13.8 KB
18 - Statistics - Inferential Statistics Confidence Intervals/010 3.13.Confidence-intervals.Two-means.Dependent-samples-exercise.xlsx 13.7 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/012 sklearn-Multiple-Linear-Regression-Summary-Table.ipynb 13.7 KB
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35 - Advanced Statistical Methods - Practical Example Linear Regression/008 Practical Example Linear Regression (Part 5)_en.srt 13.4 KB
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33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/003 Multiple-Linear-Regression-Exercise-Solution.ipynb 13.4 KB
51 - Deep Learning - Business Case Example/001 Business Case Exploring the Dataset and Identifying Predictors_en.srt 13.3 KB
02 - The Field of Data Science - The Various Data Science Disciplines/003 Business Analytics, Data Analytics, and Data Science An Introduction_en.srt 13.3 KB
55 - Appendix Deep Learning - TensorFlow 1 Business Case/001 Business Case Getting Acquainted with the Dataset_en.srt 13.2 KB
43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/004 Basic NN Example (Part 4)_en.srt 13.2 KB
05 - The Field of Data Science - Popular Data Science Techniques/001 Techniques for Working with Traditional Data_en.srt 13.2 KB
15 - Statistics - Descriptive Statistics/006 2.4.Numerical-variables.Frequency-distribution-table-exercise-solution.xlsx 13.2 KB
56 - Software Integration/003 Taking a Closer Look at APIs_en.srt 13.1 KB
63 - Appendix - pandas Fundamentals/001 Introduction to pandas Series_en.srt 13.1 KB
54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/009 12.9.TensorFlow-MNIST-with-comments.ipynb 13.0 KB
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34 - Advanced Statistical Methods - Linear Regression with sklearn/010 sklearn-Feature-Selection-with-F-regression-with-comments.ipynb 13.0 KB
43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/005 Minimal-example-All-Exercises.ipynb 12.9 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/014 SKLEAR-1.IPY 12.9 KB
20 - Statistics - Hypothesis Testing/011 4.7.Test-for-the-mean.Dependent-samples-exercise.xlsx 12.8 KB
55 - Appendix Deep Learning - TensorFlow 1 Business Case/008 TensorFlow-Audiobooks-optimizing-the-algorithm-with-comments.ipynb 12.7 KB
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34 - Advanced Statistical Methods - Linear Regression with sklearn/011 sklearn-How-to-properly-include-p-values.ipynb 12.7 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/016 Classifying the Various Reasons for Absence_en.srt 12.7 KB
63 - Appendix - pandas Fundamentals/010 Data Selection in pandas DataFrames_en.srt 12.7 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/011 Obtaining Dummies from a Single Feature_en.srt 12.7 KB
54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/008 MNIST Learning_en.srt 12.6 KB
20 - Statistics - Hypothesis Testing/009 4.6.Test-for-the-mean.Population-variance-unknown-exercise-solution.xlsx 12.6 KB
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15 - Statistics - Descriptive Statistics/018 2.10.Standard-deviation-and-coefficient-of-variation-exercise-solution.xlsx 12.6 KB
12 - Probability - Distributions/002 Types of Probability Distributions_en.srt 12.6 KB
28 - Python - Sequences/001 Lists_en.srt 12.5 KB
50 - Deep Learning - Classifying on the MNIST Dataset/010 TensorFlow-MNIST-Part6-with-comments.ipynb 12.5 KB
61 - Case Study - Analyzing the Predicted Outputs in Tableau/002 Analyzing Age vs Probability in Tableau_en.srt 12.3 KB
13 - Probability - Probability in Other Fields/001 Probability in Finance_en.srt 12.2 KB
53 - Appendix Deep Learning - TensorFlow 1 Introduction/009 5.6.TensorFlow-Minimal-example-complete.ipynb 12.1 KB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/011 Dealing with Categorical Data - Dummy Variables_en.srt 12.0 KB
17 - Statistics - Inferential Statistics Fundamentals/005 3.4.Standard-normal-distribution-exercise.xlsx 12.0 KB
51 - Deep Learning - Business Case Example/011 TensorFlow-Audiobooks-Machine-Learning-with-comments.ipynb 12.0 KB
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38 - Advanced Statistical Methods - K-Means Clustering/002 A Simple Example of Clustering_en.srt 11.8 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/014 sklearn-Feature-Selection-through-Feature-Scaling-Standardization-Part-1.ipynb 11.7 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/019 Train - Test Split Explained_en.srt 11.7 KB
64 - Appendix - Working with Text Files in Python/016 Importing Data - Partial Cleaning While Importing Data_en.srt 11.7 KB
36 - Advanced Statistical Methods - Logistic Regression/012 Accuracy-with-comments.ipynb 11.7 KB
15 - Statistics - Descriptive Statistics/018 2.10.Standard-deviation-and-coefficient-of-variation-exercise.xlsx 11.6 KB
18 - Statistics - Inferential Statistics Confidence Intervals/002 Confidence Intervals; Population Variance Known; Z-score_en.srt 11.6 KB
64 - Appendix - Working with Text Files in Python/015 Importing-Text-Data-with-NumPy-Complete.ipynb 11.6 KB
61 - Case Study - Analyzing the Predicted Outputs in Tableau/004 Analyzing Reasons vs Probability in Tableau_en.srt 11.5 KB
54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/008 12.8.TensorFlow-MNIST-with-comments-Part-6.ipynb 11.5 KB
50 - Deep Learning - Classifying on the MNIST Dataset/006 MNIST Preprocess the Data - Shuffle and Batch_en.srt 11.5 KB
15 - Statistics - Descriptive Statistics/005 2.4.Numerical-variables.Frequency-distribution-table-lesson.xlsx 11.4 KB
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20 - Statistics - Hypothesis Testing/013 4.8.Test-for-the-mean.Independent-samples-Part-1-exercise-solution.xlsx 11.2 KB
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20 - Statistics - Hypothesis Testing/006 4.4.Test-for-the-mean.Population-variance-known-exercise-solution.xlsx 11.2 KB
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51 - Deep Learning - Business Case Example/004 TensorFlow-Audiobooks-Preprocessing-with-comments.ipynb 11.2 KB
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22 - Part 4 Introduction to Python/004 Installing Python and Jupyter_en.srt 11.1 KB
64 - Appendix - Working with Text Files in Python/013 Importing .csv Files - Part III_en.srt 11.1 KB
18 - Statistics - Inferential Statistics Confidence Intervals/007 3.11.Population-variance-unknown-t-score-exercise-solution.xlsx 11.1 KB
54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/004 MNIST Model Outline_en.srt 11.1 KB
03 - The Field of Data Science - Connecting the Data Science Disciplines/001 Applying Traditional Data, Big Data, BI, Traditional Data Science and ML_en.srt 11.1 KB
15 - Statistics - Descriptive Statistics/016 2.9.Variance-exercise-solution.xlsx 11.1 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/014 Feature Scaling (Standardization)_en.srt 11.0 KB
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12 - Probability - Distributions/008 Characteristics of Continuous Distributions_en.srt 11.0 KB
40 - Part 6 Mathematics/010 Dot Product of Matrices_en.srt 11.0 KB
50 - Deep Learning - Classifying on the MNIST Dataset/009 TensorFlow-MNIST-Part5-with-comments.ipynb 11.0 KB
56 - Software Integration/002 What are Data Connectivity, APIs, and Endpoints_en.srt 11.0 KB
38 - Advanced Statistical Methods - K-Means Clustering/012 Market Segmentation with Cluster Analysis (Part 2)_en.srt 11.0 KB
15 - Statistics - Descriptive Statistics/017 2.10.Standard-deviation-and-coefficient-of-variation-lesson.xlsx 11.0 KB
13 - Probability - Probability in Other Fields/002 Probability in Statistics_en.srt 11.0 KB
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15 - Statistics - Descriptive Statistics/012 2.7.Mean-median-and-mode-exercise.xlsx 10.9 KB
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20 - Statistics - Hypothesis Testing/013 4.8.Test-for-the-mean.Independent-samples-Part-1-exercise.xlsx 10.8 KB
38 - Advanced Statistical Methods - K-Means Clustering/015 Species-Segmentation-with-Cluster-Analysis-Part-2-Exercise.ipynb 10.7 KB
28 - Python - Sequences/005 Dictionaries_en.srt 10.7 KB
09 - Part 2 Probability/001 The Basic Probability Formula_en.srt 10.7 KB
55 - Appendix Deep Learning - TensorFlow 1 Business Case/008 TensorFlow-Audiobooks-optimizing-the-algorithm.ipynb 10.6 KB
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64 - Appendix - Working with Text Files in Python/011 Importing .csv Files - Part I_en.srt 10.6 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/026 Analyzing the Dates from the Initial Data Set_en.srt 10.6 KB
18 - Statistics - Inferential Statistics Confidence Intervals/007 3.11.Population-variance-unknown-t-score-exercise.xlsx 10.6 KB
42 - Deep Learning - Introduction to Neural Networks/011 Optimization Algorithm 1-Parameter Gradient Descent_en.srt 10.6 KB
12 - Probability - Distributions/006 Discrete Distributions The Binomial Distribution_en.srt 10.6 KB
20 - Statistics - Hypothesis Testing/015 4.9.Test-for-the-mean.Independent-samples-Part-2-exercise-2.xlsx 10.5 KB
05 - The Field of Data Science - Popular Data Science Techniques/005 Business Intelligence (BI) Techniques_en.srt 10.5 KB
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18 - Statistics - Inferential Statistics Confidence Intervals/009 3.13.Confidence-intervals.Two-means.Dependent-samples-lesson.xlsx 10.5 KB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/002 Creating the Targets for the Logistic Regression_en.srt 10.4 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/010 sklearn-Feature-Selection-with-F-regression.ipynb 10.4 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/008 sklearn-Multiple-Linear-Regression-and-Adjusted-R-squared-with-comments.ipynb 10.4 KB
21 - Statistics - Practical Example Hypothesis Testing/001 Practical Example Hypothesis Testing_en.srt 10.4 KB
17 - Statistics - Inferential Statistics Fundamentals/004 3.4.Standard-normal-distribution-lesson.xlsx 10.4 KB
29 - Python - Iterations/003 Lists with the range() Function_en.srt 10.4 KB
55 - Appendix Deep Learning - TensorFlow 1 Business Case/007 TensorFlow-Audiobooks-Outlining-the-model-with-comments.ipynb 10.3 KB
38 - Advanced Statistical Methods - K-Means Clustering/005 Categorical.csv 10.3 KB
20 - Statistics - Hypothesis Testing/003 Rejection Region and Significance Level_en.srt 10.3 KB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/008 Interpreting the Coefficients for Our Problem_en.srt 10.3 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/009 sklearn-Multiple-Linear-Regression-and-Adjusted-R-squared-Exercise-Solution.ipynb 10.3 KB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/005 Splitting the Data for Training and Testing_en.srt 10.3 KB
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62 - Appendix - Additional Python Tools/003 Introduction to Nested For Loops_en.srt 10.2 KB
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44 - Deep Learning - TensorFlow 2.0 Introduction/006 Outlining the Model with TensorFlow 2_en.srt 10.2 KB
18 - Statistics - Inferential Statistics Confidence Intervals/009 Confidence intervals. Two means. Dependent samples_en.srt 10.2 KB
18 - Statistics - Inferential Statistics Confidence Intervals/012 3.14.Confidence-intervals.Two-means.Independent-samples-Part-1-exercise-solution.xlsx 10.1 KB
12 - Probability - Distributions/001 Fundamentals of Probability Distributions_en.srt 10.1 KB
15 - Statistics - Descriptive Statistics/015 2.9.Variance-lesson.xlsx 10.1 KB
32 - Advanced Statistical Methods - Linear Regression with StatsModels/001 The Linear Regression Model_en.srt 10.1 KB
51 - Deep Learning - Business Case Example/009 TensorFlow-Audiobooks-Machine-Learning-Part3-with-comments.ipynb 10.1 KB
51 - Deep Learning - Business Case Example/005 TensorFlow-Audiobooks-Preprocessing-Exercise-Solution.ipynb 10.0 KB
55 - Appendix Deep Learning - TensorFlow 1 Business Case/005 TensorFlow-Audiobooks-Preprocessing-Exercise-Solution.ipynb 10.0 KB
63 - Appendix - pandas Fundamentals/011 pandas DataFrames - Indexing with .iloc[]_en.srt 10.0 KB
35 - Advanced Statistical Methods - Practical Example Linear Regression/002 Practical Example Linear Regression (Part 2)_en.srt 10.0 KB
64 - Appendix - Working with Text Files in Python/021 Importing Data in Python - an Important Exercise_en.srt 10.0 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/007 Dropping a Column from a DataFrame in Python_en.srt 10.0 KB
32 - Advanced Statistical Methods - Linear Regression with StatsModels/005 First Regression in Python_en.srt 10.0 KB
55 - Appendix Deep Learning - TensorFlow 1 Business Case/006 Creating a Data Provider_en.srt 9.9 KB
15 - Statistics - Descriptive Statistics/015 Variance_en.srt 9.9 KB
54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/009 MNIST Results and Testing_en.srt 9.9 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/009 sklearn-Multiple-Linear-Regression-and-Adjusted-R-squared-Exercise.ipynb 9.8 KB
64 - Appendix - Working with Text Files in Python/025 Saving-Data-NP-Complete.ipynb 9.8 KB
18 - Statistics - Inferential Statistics Confidence Intervals/011 3.14.Confidence-intervals.Two-means.Independent-samples-Part-1-lesson.xlsx 9.8 KB
18 - Statistics - Inferential Statistics Confidence Intervals/012 3.14.Confidence-intervals.Two-means.Independent-samples-Part-1-exercise.xlsx 9.8 KB
18 - Statistics - Inferential Statistics Confidence Intervals/014 3.15.Confidence-intervals.Two-means.Independent-samples-Part-2-exercise-solution.xlsx 9.8 KB
20 - Statistics - Hypothesis Testing/010 4.7.Test-for-the-mean.Dependent-samples-lesson.xlsx 9.8 KB
20 - Statistics - Hypothesis Testing/005 Test for the Mean. Population Variance Known_en.srt 9.8 KB
51 - Deep Learning - Business Case Example/009 Business Case Setting an Early Stopping Mechanism_en.srt 9.7 KB
12 - Probability - Distributions/015 Customers-Membership.xlsx 9.7 KB
60 - Case Study - Loading the 'absenteeism_module'/003 Deploying the 'absenteeism_module' - Part II_en.srt 9.7 KB
53 - Appendix Deep Learning - TensorFlow 1 Introduction/007 Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases_en.srt 9.7 KB
06 - The Field of Data Science - Popular Data Science Tools/001 Necessary Programming Languages and Software Used in Data Science_en.srt 9.6 KB
20 - Statistics - Hypothesis Testing/012 4.8.Test-for-the-mean.Independent-samples-Part-1-lesson.xlsx 9.6 KB
63 - Appendix - pandas Fundamentals/008 Introduction to pandas DataFrames - Part II_en.srt 9.6 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/027 Extracting the Month Value from the Date Column_en.srt 9.6 KB
50 - Deep Learning - Classifying on the MNIST Dataset/010 MNIST Learning_en.srt 9.6 KB
29 - Python - Iterations/004 Conditional Statements and Loops_en.srt 9.6 KB
12 - Probability - Distributions/015 Daily-Views.xlsx 9.5 KB
18 - Statistics - Inferential Statistics Confidence Intervals/013 3.15.Confidence-intervals.Two-means.Independent-samples-Part-2-lesson.xlsx 9.5 KB
15 - Statistics - Descriptive Statistics/014 2.8.Skewness-exercise.xlsx 9.5 KB
53 - Appendix Deep Learning - TensorFlow 1 Introduction/009 Basic NN Example with TF Model Output_en.srt 9.5 KB
22 - Part 4 Introduction to Python/006 Prerequisites for Coding in the Jupyter Notebooks_en.srt 9.4 KB
29 - Python - Iterations/006 How to Iterate over Dictionaries_en.srt 9.4 KB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/013 Making-predictions-with-comments.ipynb 9.4 KB
55 - Appendix Deep Learning - TensorFlow 1 Business Case/007 TensorFlow-Audiobooks-Outlining-the-model.ipynb 9.4 KB
42 - Deep Learning - Introduction to Neural Networks/012 Optimization Algorithm n-Parameter Gradient Descent_en.srt 9.3 KB
20 - Statistics - Hypothesis Testing/014 4.9.Test-for-the-mean.Independent-samples-Part-2-lesson.xlsx 9.3 KB
23 - Python - Variables and Data Types/003 Python Strings_en.srt 9.3 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/015 Feature Selection through Standardization of Weights_en.srt 9.2 KB
61 - Case Study - Analyzing the Predicted Outputs in Tableau/006 Analyzing Transportation Expense vs Probability in Tableau_en.srt 9.2 KB
11 - Probability - Bayesian Inference/011 Bayes' Law_en.srt 9.2 KB
18 - Statistics - Inferential Statistics Confidence Intervals/014 3.15.Confidence-intervals.Two-means.Independent-samples-Part-2-exercise.xlsx 9.2 KB
48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/temp/index_13061118_1920x1080.m3u8 9.1 KB
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34 - Advanced Statistical Methods - Linear Regression with sklearn/008 sklearn-Multiple-Linear-Regression-and-Adjusted-R-squared.ipynb 9.1 KB
48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/temp/index_13061118_768x432.m3u8 9.1 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/003 Simple Linear Regression with sklearn_en.srt 9.1 KB
39 - Advanced Statistical Methods - Other Types of Clustering/002 Dendrogram_en.srt 9.1 KB
64 - Appendix - Working with Text Files in Python/025 Saving Your Data with NumPy - Part I - .npy_en.srt 9.1 KB
48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/temp/index_13061118_640x360.m3u8 9.1 KB
38 - Advanced Statistical Methods - K-Means Clustering/006 How to Choose the Number of Clusters_en.srt 9.1 KB
64 - Appendix - Working with Text Files in Python/018 Importing Data from .json Files_en.srt 9.1 KB
38 - Advanced Statistical Methods - K-Means Clustering/011 Market Segmentation with Cluster Analysis (Part 1)_en.srt 9.1 KB
44 - Deep Learning - TensorFlow 2.0 Introduction/006 TensorFlow-Minimal-example-Part2.ipynb 9.1 KB
64 - Appendix - Working with Text Files in Python/005 Importing Data in Python - Principles_en.srt 9.1 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/019 sklearn-Train-Test-Split-with-comments.ipynb 9.0 KB
28 - Python - Sequences/004 Tuples_en.srt 9.0 KB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/002 Adjusted R-Squared_en.srt 9.0 KB
02 - The Field of Data Science - The Various Data Science Disciplines/001 Data Science and Business Buzzwords Why are there so Many_en.srt 9.0 KB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/010 Interpreting the Coefficients of the Logistic Regression_en.srt 9.0 KB
64 - Appendix - Working with Text Files in Python/010 Importing Text Files - with open()_en.srt 8.9 KB
63 - Appendix - pandas Fundamentals/007 Introduction to pandas DataFrames - Part I_en.srt 8.8 KB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/006 Fitting the Model and Assessing its Accuracy_en.srt 8.8 KB
22 - Part 4 Introduction to Python/001 Introduction to Programming_en.srt 8.7 KB
12 - Probability - Distributions/007 Discrete Distributions The Poisson Distribution_en.srt 8.7 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/007 sklearn-Multiple-Linear-Regression-with-comments.ipynb 8.7 KB
53 - Appendix Deep Learning - TensorFlow 1 Introduction/008 5.5.TensorFlow-Minimal-example-Part-3.ipynb 8.6 KB
63 - Appendix - pandas Fundamentals/002 Working with Methods in Python - Part I_en.srt 8.6 KB
22 - Part 4 Introduction to Python/002 Why Python_en.srt 8.6 KB
50 - Deep Learning - Classifying on the MNIST Dataset/007 TensorFlow-MNIST-Part3-with-comments.ipynb 8.6 KB
55 - Appendix Deep Learning - TensorFlow 1 Business Case/007 Business Case Model Outline_en.srt 8.6 KB
51 - Deep Learning - Business Case Example/005 TensorFlow-Audiobooks-Preprocessing-Exercise.ipynb 8.6 KB
55 - Appendix Deep Learning - TensorFlow 1 Business Case/005 TensorFlow-Audiobooks-Preprocessing-Exercise.ipynb 8.6 KB
50 - Deep Learning - Classifying on the MNIST Dataset/008 MNIST Outline the Model_en.srt 8.6 KB
20 - Statistics - Hypothesis Testing/001 Null vs Alternative Hypothesis_en.srt 8.6 KB
13 - Probability - Probability in Other Fields/003 Probability in Data Science_en.srt 8.5 KB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/008 A3 Normality and Homoscedasticity_en.srt 8.5 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/003 Checking the Content of the Data Set_en.srt 8.5 KB
54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/007 12.7.TensorFlow-MNIST-with-comments-Part-5.ipynb 8.5 KB
09 - Part 2 Probability/004 Events and Their Complements_en.srt 8.5 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/032 Absenteeism-Exercise-Preprocessing-df-preprocessed.ipynb 8.5 KB
38 - Advanced Statistical Methods - K-Means Clustering/007 How-to-Choose-the-Number-of-Clusters-Solution.ipynb 8.5 KB
30 - Python - Advanced Python Tools/001 Object Oriented Programming_en.srt 8.4 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/010 Feature Selection (F-regression)_en.srt 8.4 KB
09 - Part 2 Probability/003 Frequency_en.srt 8.3 KB
09 - Part 2 Probability/002 Computing Expected Values_en.srt 8.3 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/029 Absenteeism-Exercise-Removing-the-Date-Column-SOLUTION.ipynb 8.3 KB
55 - Appendix Deep Learning - TensorFlow 1 Business Case/008 Business Case Optimization_en.srt 8.3 KB
56 - Software Integration/005 Software Integration - Explained_en.srt 8.3 KB
46 - Deep Learning - Overfitting/006 Early Stopping or When to Stop Training_en.srt 8.3 KB
36 - Advanced Statistical Methods - Logistic Regression/016 Bank-data-testing.csv 8.3 KB
38 - Advanced Statistical Methods - K-Means Clustering/003 Countries-exercise.csv 8.3 KB
38 - Advanced Statistical Methods - K-Means Clustering/007 Countries-exercise.csv 8.3 KB
45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/003 Digging into a Deep Net_en.srt 8.3 KB
15 - Statistics - Descriptive Statistics/009 Cross Tables and Scatter Plots_en.srt 8.3 KB
64 - Appendix - Working with Text Files in Python/006 Plain Text Files, Flat Files and More_en.srt 8.3 KB
26 - Python - Conditional Statements/003 The ELIF Statement_en.srt 8.2 KB
01 - Part 1 Introduction/001 A Practical Example What You Will Learn in This Course_en.srt 8.2 KB
44 - Deep Learning - TensorFlow 2.0 Introduction/007 Interpreting the Result and Extracting the Weights and Bias_en.srt 8.2 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/004 Simple Linear Regression with sklearn - A StatsModels-like Summary Table_en.srt 8.1 KB
20 - Statistics - Hypothesis Testing/010 Test for the Mean. Dependent Samples_en.srt 8.1 KB
32 - Advanced Statistical Methods - Linear Regression with StatsModels/011 R-Squared_en.srt 8.1 KB
38 - Advanced Statistical Methods - K-Means Clustering/013 How is Clustering Useful_en.srt 8.1 KB
29 - Python - Iterations/001 For Loops_en.srt 8.1 KB
43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/002 Basic NN Example (Part 2)_en.srt 8.0 KB
15 - Statistics - Descriptive Statistics/003 Categorical Variables - Visualization Techniques_en.srt 8.0 KB
64 - Appendix - Working with Text Files in Python/026 Saving Your Data with NumPy - Part II - .npz_en.srt 8.0 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/008 Calculating the Adjusted R-Squared in sklearn_en.srt 8.0 KB
38 - Advanced Statistical Methods - K-Means Clustering/001 K-Means Clustering_en.srt 7.9 KB
44 - Deep Learning - TensorFlow 2.0 Introduction/001 How to Install TensorFlow 2.0_en.srt 7.9 KB
54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/006 12.6.TensorFlow-MNIST-with-comments-Part-4.ipynb 7.9 KB
63 - Appendix - pandas Fundamentals/009 pandas DataFrames - Common Attributes_en.srt 7.8 KB
36 - Advanced Statistical Methods - Logistic Regression/015 Testing the Model_en.srt 7.8 KB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/012 Testing the Model We Created_en.srt 7.8 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/007 sklearn-Multiple-Linear-Regression.ipynb 7.8 KB
50 - Deep Learning - Classifying on the MNIST Dataset/004 MNIST Preprocess the Data - Create a Validation Set and Scale It_en.srt 7.8 KB
04 - The Field of Data Science - The Benefits of Each Discipline/001 The Reason Behind These Disciplines_en.srt 7.8 KB
62 - Appendix - Additional Python Tools/002 Iterating Over Range Objects_en.srt 7.8 KB
18 - Statistics - Inferential Statistics Confidence Intervals/008 Margin of Error_en.srt 7.7 KB
51 - Deep Learning - Business Case Example/008 Business Case Learning and Interpreting the Result_en.srt 7.7 KB
32 - Advanced Statistical Methods - Linear Regression with StatsModels/008 How to Interpret the Regression Table_en.srt 7.6 KB
48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/004 Learning Rate Schedules, or How to Choose the Optimal Learning Rate_en.srt 7.6 KB
15 - Statistics - Descriptive Statistics/017 Standard Deviation and Coefficient of Variation_en.srt 7.6 KB
36 - Advanced Statistical Methods - Logistic Regression/015 Testing-the-model-with-comments.ipynb 7.6 KB
23 - Python - Variables and Data Types/003 Strings-Lecture-Py3.ipynb 7.6 KB
38 - Advanced Statistical Methods - K-Means Clustering/009 To Standardize or not to Standardize_en.srt 7.6 KB
56 - Software Integration/001 What are Data, Servers, Clients, Requests, and Responses_en.srt 7.5 KB
18 - Statistics - Inferential Statistics Confidence Intervals/011 Confidence intervals. Two means. Independent Samples (Part 1)_en.srt 7.5 KB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/007 Creating a Summary Table with the Coefficients and Intercept_en.srt 7.5 KB
52 - Deep Learning - Conclusion/004 An overview of CNNs_en.srt 7.5 KB
37 - Advanced Statistical Methods - Cluster Analysis/002 Some Examples of Clusters_en.srt 7.5 KB
38 - Advanced Statistical Methods - K-Means Clustering/006 Selecting-the-number-of-clusters-with-comments.ipynb 7.5 KB
42 - Deep Learning - Introduction to Neural Networks/001 Introduction to Neural Networks_en.srt 7.5 KB
11 - Probability - Bayesian Inference/004 Union of Sets_en.srt 7.5 KB
64 - Appendix - Working with Text Files in Python/022 Customer-Gender.csv 7.4 KB
40 - Part 6 Mathematics/004 Arrays in Python - A Convenient Way To Represent Matrices_en.srt 7.4 KB
39 - Advanced Statistical Methods - Other Types of Clustering/003 Heatmaps_en.srt 7.4 KB
49 - Deep Learning - Preprocessing/003 Standardization_en.srt 7.4 KB
38 - Advanced Statistical Methods - K-Means Clustering/014 Species-Segmentation-with-Cluster-Analysis-Part-1-Solution.ipynb 7.4 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/029 Absenteeism-Exercise-Preprocessing-ChP-df-date-reason-mod.ipynb 7.3 KB
54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/005 12.5.TensorFlow-MNIST-with-comments-Part-3.ipynb 7.3 KB
29 - Python - Iterations/002 While Loops and Incrementing_en.srt 7.3 KB
10 - Probability - Combinatorics/006 Solving Combinations_en.srt 7.2 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/019 sklearn-Train-Test-Split.ipynb 7.2 KB
25 - Python - Other Python Operators/002 Logical and Identity Operators_en.srt 7.2 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/010 Analyzing the Reasons for Absence_en.srt 7.2 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/031 Working on Education, Children, and Pets_en.srt 7.2 KB
15 - Statistics - Descriptive Statistics/011 Mean, median and mode_en.srt 7.2 KB
20 - Statistics - Hypothesis Testing/008 Test for the Mean. Population Variance Unknown_en.srt 7.2 KB
11 - Probability - Bayesian Inference/007 The Conditional Probability Formula_en.srt 7.2 KB
50 - Deep Learning - Classifying on the MNIST Dataset/012 MNIST Testing the Model_en.srt 7.1 KB
64 - Appendix - Working with Text Files in Python/001 An Introduction to Working with Files in Python_en.srt 7.1 KB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/016 Preparing the Deployment of the Model through a Module_en.srt 7.1 KB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/011 Dummy-variables-with-comments.ipynb 7.1 KB
36 - Advanced Statistical Methods - Logistic Regression/002 A Simple Example in Python_en.srt 7.1 KB
17 - Statistics - Inferential Statistics Fundamentals/002 What is a Distribution_en.srt 7.1 KB
64 - Appendix - Working with Text Files in Python/008 Common Naming Conventions_en.srt 7.1 KB
56 - Software Integration/004 Communication between Software Products through Text Files_en.srt 7.0 KB
14 - Part 3 Statistics/001 Population and Sample_en.srt 7.0 KB
63 - Appendix - pandas Fundamentals/005 Using .unique() and .nunique()_en.srt 7.0 KB
05 - The Field of Data Science - Popular Data Science Techniques/003 Techniques for Working with Big Data_en.srt 7.0 KB
46 - Deep Learning - Overfitting/001 What is Overfitting_en.srt 7.0 KB
15 - Statistics - Descriptive Statistics/001 Types of Data_en.srt 6.9 KB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/013 Saving the Model and Preparing it for Deployment_en.srt 6.9 KB
52 - Deep Learning - Conclusion/006 An Overview of non-NN Approaches_en.srt 6.9 KB
63 - Appendix - pandas Fundamentals/004 Parameters and Arguments in pandas_en.srt 6.9 KB
12 - Probability - Distributions/010 Continuous Distributions The Standard Normal Distribution_en.srt 6.8 KB
38 - Advanced Statistical Methods - K-Means Clustering/012 Market-segmentation-example-Part2-with-comments.ipynb 6.8 KB
43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/003 Minimal-example-Part-3.ipynb 6.8 KB
36 - Advanced Statistical Methods - Logistic Regression/016 Testing-the-Model-Exercise.ipynb 6.8 KB
50 - Deep Learning - Classifying on the MNIST Dataset/012 TensorFlow-MNIST-complete.ipynb 6.8 KB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/007 A2 No Endogeneity_en.srt 6.7 KB
48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/006 Adaptive Learning Rate Schedules (AdaGrad and RMSprop )_en.srt 6.7 KB
18 - Statistics - Inferential Statistics Confidence Intervals/004 Confidence Interval Clarifications_en.srt 6.7 KB
36 - Advanced Statistical Methods - Logistic Regression/007 Understanding Logistic Regression Tables_en.srt 6.7 KB
17 - Statistics - Inferential Statistics Fundamentals/006 Central Limit Theorem_en.srt 6.7 KB
40 - Part 6 Mathematics/008 Transpose of a Matrix_en.srt 6.7 KB
63 - Appendix - pandas Fundamentals/012 pandas DataFrames - Indexing with .loc[]_en.srt 6.7 KB
32 - Advanced Statistical Methods - Linear Regression with StatsModels/004 Python Packages Installation_en.srt 6.7 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/016 Predicting with the Standardized Coefficients_en.srt 6.6 KB
64 - Appendix - Working with Text Files in Python/019 An Introduction to Working with Excel Files in Python_en.srt 6.6 KB
57 - Case Study - What's Next in the Course/001 Game Plan for this Python, SQL, and Tableau Business Exercise_en.srt 6.6 KB
63 - Appendix - pandas Fundamentals/006 Using .sort_values()_en.srt 6.6 KB
60 - Case Study - Loading the 'absenteeism_module'/001 absenteeism-module.py 6.6 KB
28 - Python - Sequences/003 List Slicing_en.srt 6.6 KB
20 - Statistics - Hypothesis Testing/004 Type I Error and Type II Error_en.srt 6.6 KB
44 - Deep Learning - TensorFlow 2.0 Introduction/002 TensorFlow Outline and Comparison with Other Libraries_en.srt 6.6 KB
08 - The Field of Data Science - Debunking Common Misconceptions/001 Debunking Common Misconceptions_en.srt 6.6 KB
20 - Statistics - Hypothesis Testing/012 Test for the mean. Independent Samples (Part 1)_en.srt 6.5 KB
42 - Deep Learning - Introduction to Neural Networks/003 Types of Machine Learning_en.srt 6.5 KB
01 - Part 1 Introduction/002 What Does the Course Cover_en.srt 6.5 KB
11 - Probability - Bayesian Inference/001 Sets and Events_en.srt 6.5 KB
52 - Deep Learning - Conclusion/001 Summary on What You've Learned_en.srt 6.5 KB
12 - Probability - Distributions/005 Discrete Distributions The Bernoulli Distribution_en.srt 6.5 KB
49 - Deep Learning - Preprocessing/005 Binary and One-Hot Encoding_en.srt 6.5 KB
20 - Statistics - Hypothesis Testing/014 Test for the mean. Independent Samples (Part 2)_en.srt 6.5 KB
64 - Appendix - Working with Text Files in Python/027 Saving Your Data with NumPy - Part III - .csv_en.srt 6.5 KB
18 - Statistics - Inferential Statistics Confidence Intervals/006 Confidence Intervals; Population Variance Unknown; T-score_en.srt 6.5 KB
42 - Deep Learning - Introduction to Neural Networks/010 Common Objective Functions Cross-Entropy Loss_en.srt 6.5 KB
12 - Probability - Distributions/014 Continuous Distributions The Logistic Distribution_en.srt 6.4 KB
55 - Appendix Deep Learning - TensorFlow 1 Business Case/011 Business Case A Comment on the Homework_en.srt 6.4 KB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/011 Backward Elimination or How to Simplify Your Model_en.srt 6.4 KB
50 - Deep Learning - Classifying on the MNIST Dataset/005 TensorFlow-MNIST-Part2-with-comments.ipynb 6.4 KB
54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/006 Calculating the Accuracy of the Model_en.srt 6.3 KB
53 - Appendix Deep Learning - TensorFlow 1 Introduction/004 TensorFlow Intro_en.srt 6.3 KB
20 - Statistics - Hypothesis Testing/007 p-value_en.srt 6.3 KB
36 - Advanced Statistical Methods - Logistic Regression/010 Binary Predictors in a Logistic Regression_en.srt 6.3 KB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/009 Standardizing only the Numerical Variables (Creating a Custom Scaler)_en.srt 6.3 KB
45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/005 Activation Functions_en.srt 6.3 KB
17 - Statistics - Inferential Statistics Fundamentals/003 The Normal Distribution_en.srt 6.2 KB
36 - Advanced Statistical Methods - Logistic Regression/005 Example-bank-data.csv 6.2 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/017 Using .concat() in Python_en.srt 6.2 KB
36 - Advanced Statistical Methods - Logistic Regression/014 Underfitting and Overfitting_en.srt 6.2 KB
53 - Appendix Deep Learning - TensorFlow 1 Introduction/007 5.4.TensorFlow-Minimal-example-Part-2.ipynb 6.2 KB
28 - Python - Sequences/005 Dictionaries-Solution-Py3.ipynb 6.2 KB
15 - Statistics - Descriptive Statistics/019 Covariance_en.srt 6.1 KB
02 - The Field of Data Science - The Various Data Science Disciplines/002 What is the difference between Analysis and Analytics_en.srt 6.1 KB
54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/004 12.4.TensorFlow-MNIST-with-comments-Part-2.ipynb 6.1 KB
12 - Probability - Distributions/009 Continuous Distributions The Normal Distribution_en.srt 6.1 KB
39 - Advanced Statistical Methods - Other Types of Clustering/001 Types of Clustering_en.srt 6.1 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/017 sklearn-Feature-Scaling-Exercise.ipynb 6.1 KB
02 - The Field of Data Science - The Various Data Science Disciplines/005 A Breakdown of our Data Science Infographic_en.srt 6.1 KB
36 - Advanced Statistical Methods - Logistic Regression/003 Logistic vs Logit Function_en.srt 6.1 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/003 sklearn-Simple-Linear-Regression-with-comments.ipynb 6.1 KB
64 - Appendix - Working with Text Files in Python/003 Structured, Semi-Structured and Unstructured Data_en.srt 6.0 KB
64 - Appendix - Working with Text Files in Python/028 Saving-Data-NP-Exercise.ipynb 6.0 KB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/009 A4 No Autocorrelation_en.srt 6.0 KB
46 - Deep Learning - Overfitting/003 What is Validation_en.srt 6.0 KB
60 - Case Study - Loading the 'absenteeism_module'/002 Deploying the 'absenteeism_module' - Part I_en.srt 5.9 KB
15 - Statistics - Descriptive Statistics/021 Correlation Coefficient_en.srt 5.9 KB
10 - Probability - Combinatorics/005 Solving Variations without Repetition_en.srt 5.9 KB
37 - Advanced Statistical Methods - Cluster Analysis/001 Introduction to Cluster Analysis_en.srt 5.9 KB
22 - Part 4 Introduction to Python/003 Why Jupyter_en.srt 5.9 KB
38 - Advanced Statistical Methods - K-Means Clustering/011 Market-segmentation-example-with-comments.ipynb 5.9 KB
30 - Python - Advanced Python Tools/004 Importing Modules in Python_en.srt 5.9 KB
25 - Python - Other Python Operators/002 Logical-and-Identity-Operators-Lecture-Py3.ipynb 5.9 KB
53 - Appendix Deep Learning - TensorFlow 1 Introduction/008 Basic NN Example with TF Loss Function and Gradient Descent_en.srt 5.8 KB
38 - Advanced Statistical Methods - K-Means Clustering/002 Country-clusters-with-comments.ipynb 5.8 KB
41 - Part 7 Deep Learning/001 What to Expect from this Part_en.srt 5.8 KB
48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/001 Stochastic Gradient Descent_en.srt 5.8 KB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/001 Exploring the Problem with a Machine Learning Mindset_en.srt 5.8 KB
64 - Appendix - Working with Text Files in Python/023 Importing Files in Jupyter_en.srt 5.8 KB
42 - Deep Learning - Introduction to Neural Networks/006 The Linear model with Multiple Inputs and Multiple Outputs_en.srt 5.8 KB
23 - Python - Variables and Data Types/001 Variables_en.srt 5.8 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/028 Extracting the Day of the Week from the Date Column_en.srt 5.8 KB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/013 Making-predictions.ipynb 5.8 KB
36 - Advanced Statistical Methods - Logistic Regression/015 Testing-the-model.ipynb 5.8 KB
15 - Statistics - Descriptive Statistics/002 Levels of Measurement_en.srt 5.8 KB
64 - Appendix - Working with Text Files in Python/024 Saving Your Data with pandas_en.srt 5.8 KB
42 - Deep Learning - Introduction to Neural Networks/002 Training the Model_en.srt 5.7 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/013 sklearn-Multiple-Linear-Regression-Exercise.ipynb 5.7 KB
64 - Appendix - Working with Text Files in Python/004 Text Files and Data Connectivity_en.srt 5.7 KB
11 - Probability - Bayesian Inference/010 The Multiplication Law_en.srt 5.6 KB
38 - Advanced Statistical Methods - K-Means Clustering/004 Categorical-data-with-comments.ipynb 5.6 KB
18 - Statistics - Inferential Statistics Confidence Intervals/013 Confidence intervals. Two means. Independent Samples (Part 2)_en.srt 5.6 KB
07 - The Field of Data Science - Careers in Data Science/001 Finding the Job - What to Expect and What to Look for_en.srt 5.6 KB
51 - Deep Learning - Business Case Example/004 TensorFlow-Audiobooks-Preprocessing.ipynb 5.6 KB
55 - Appendix Deep Learning - TensorFlow 1 Business Case/004 TensorFlow-Audiobooks-Preprocessing.ipynb 5.6 KB
51 - Deep Learning - Business Case Example/006 Business Case Load the Preprocessed Data_en.srt 5.6 KB
38 - Advanced Statistical Methods - K-Means Clustering/007 How-to-Choose-the-Number-of-Clusters-Exercise.ipynb 5.5 KB
40 - Part 6 Mathematics/001 What is a Matrix_en.srt 5.5 KB
27 - Python - Python Functions/007 Notable-Built-In-Functions-in-Python-Solution-Py3.ipynb 5.5 KB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/010 A5 No Multicollinearity_en.srt 5.5 KB
64 - Appendix - Working with Text Files in Python/022 Importing Data with the .squeeze() Method_en.srt 5.5 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/030 Analyzing Several Straightforward Columns for this Exercise_en.srt 5.5 KB
38 - Advanced Statistical Methods - K-Means Clustering/008 Pros and Cons of K-Means Clustering_en.srt 5.5 KB
11 - Probability - Bayesian Inference/002 Ways Sets Can Interact_en.srt 5.5 KB
45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/007 Backpropagation_en.srt 5.5 KB
23 - Python - Variables and Data Types/003 Strings-Solution-Py3.ipynb 5.5 KB
18 - Statistics - Inferential Statistics Confidence Intervals/005 Student's T Distribution_en.srt 5.4 KB
27 - Python - Python Functions/002 How to Create a Function with a Parameter_en.srt 5.4 KB
43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/001 Basic NN Example (Part 1)_en.srt 5.4 KB
36 - Advanced Statistical Methods - Logistic Regression/013 Calculating-the-Accuracy-of-the-Model-Exercise.ipynb 5.4 KB
10 - Probability - Combinatorics/007 Symmetry of Combinations_en.srt 5.4 KB
45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/006 Activation Functions Softmax Activation_en.srt 5.4 KB
32 - Advanced Statistical Methods - Linear Regression with StatsModels/009 Decomposition of Variability_en.srt 5.4 KB
35 - Advanced Statistical Methods - Practical Example Linear Regression/004 Practical Example Linear Regression (Part 3)_en.srt 5.4 KB
12 - Probability - Distributions/013 Continuous Distributions The Exponential Distribution_en.srt 5.4 KB
37 - Advanced Statistical Methods - Cluster Analysis/004 Math Prerequisites_en.srt 5.3 KB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/013 Making Predictions with the Linear Regression_en.srt 5.3 KB
36 - Advanced Statistical Methods - Logistic Regression/002 Admittance-with-comments.ipynb 5.3 KB
15 - Statistics - Descriptive Statistics/005 Numerical Variables - Frequency Distribution Table_en.srt 5.3 KB
43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/003 Basic NN Example (Part 3)_en.srt 5.3 KB
36 - Advanced Statistical Methods - Logistic Regression/009 What do the Odds Actually Mean_en.srt 5.3 KB
10 - Probability - Combinatorics/002 Permutations and How to Use Them_en.srt 5.3 KB
55 - Appendix Deep Learning - TensorFlow 1 Business Case/003 The Importance of Working with a Balanced Dataset_en.srt 5.3 KB
24 - Python - Basic Python Syntax/001 Using Arithmetic Operators in Python_en.srt 5.2 KB
40 - Part 6 Mathematics/009 Dot Product_en.srt 5.2 KB
44 - Deep Learning - TensorFlow 2.0 Introduction/008 Customizing a TensorFlow 2 Model_en.srt 5.2 KB
46 - Deep Learning - Overfitting/005 N-Fold Cross Validation_en.srt 5.2 KB
45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/004 Non-Linearities and their Purpose_en.srt 5.2 KB
57 - Case Study - What's Next in the Course/003 Introducing the Data Set_en.srt 5.2 KB
27 - Python - Python Functions/007 Built-in Functions in Python_en.srt 5.2 KB
51 - Deep Learning - Business Case Example/003 Business Case Balancing the Dataset_en.srt 5.2 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/004 Introduction to Terms with Multiple Meanings_en.srt 5.1 KB
40 - Part 6 Mathematics/006 Addition and Subtraction of Matrices_en.srt 5.1 KB
36 - Advanced Statistical Methods - Logistic Regression/012 Calculating the Accuracy of the Model_en.srt 5.1 KB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/004 Standardizing the Data_en.srt 5.1 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/007 Multiple Linear Regression with sklearn_en.srt 5.1 KB
64 - Appendix - Working with Text Files in Python/002 File vs File Object, Reading vs Parsing Data_en.srt 5.1 KB
10 - Probability - Combinatorics/009 Combinatorics in Real-Life The Lottery_en.srt 5.1 KB
28 - Python - Sequences/003 List-Slicing-Lecture-Py3.ipynb 5.0 KB
40 - Part 6 Mathematics/003 Linear Algebra and Geometry_en.srt 4.9 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/003 sklearn-Simple-Linear-Regression.ipynb 4.9 KB
38 - Advanced Statistical Methods - K-Means Clustering/005 Clustering-Categorical-Data-Solution.ipynb 4.9 KB
57 - Case Study - What's Next in the Course/002 The Business Task_en.srt 4.9 KB
49 - Deep Learning - Preprocessing/001 Preprocessing Introduction_en.srt 4.9 KB
10 - Probability - Combinatorics/008 Solving Combinations with Separate Sample Spaces_en.srt 4.9 KB
44 - Deep Learning - TensorFlow 2.0 Introduction/003 TensorFlow 1 vs TensorFlow 2_en.srt 4.8 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/002 Importing the Absenteeism Data in Python_en.srt 4.8 KB
17 - Statistics - Inferential Statistics Fundamentals/004 The Standard Normal Distribution_en.srt 4.8 KB
40 - Part 6 Mathematics/002 Scalars and Vectors_en.srt 4.8 KB
64 - Appendix - Working with Text Files in Python/014 Importing Data with index_col_en.srt 4.8 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/023 Absenteeism-Exercise-Preprocessing-df-reason-mod.ipynb 4.8 KB
36 - Advanced Statistical Methods - Logistic Regression/008 Understanding-Logistic-Regression-Tables-Solution.ipynb 4.8 KB
17 - Statistics - Inferential Statistics Fundamentals/008 Estimators and Estimates_en.srt 4.8 KB
11 - Probability - Bayesian Inference/008 The Law of Total Probability_en.srt 4.8 KB
63 - Appendix - pandas Fundamentals/003 Working with Methods in Python - Part II_en.srt 4.7 KB
52 - Deep Learning - Conclusion/005 An Overview of RNNs_en.srt 4.7 KB
38 - Advanced Statistical Methods - K-Means Clustering/012 Market-segmentation-example-Part2.ipynb 4.7 KB
38 - Advanced Statistical Methods - K-Means Clustering/003 A-Simple-Example-of-Clustering-Solution.ipynb 4.6 KB
30 - Python - Advanced Python Tools/003 What is the Standard Library_en.srt 4.6 KB
54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/002 MNIST How to Tackle the MNIST_en.srt 4.6 KB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/011 Dummy-Variables.ipynb 4.6 KB
51 - Deep Learning - Business Case Example/007 TensorFlow-Audiobooks-Machine-Learning-Part1-with-comments.ipynb 4.6 KB
28 - Python - Sequences/004 Tuples-Solution-Py3.ipynb 4.6 KB
10 - Probability - Combinatorics/010 A Recap of Combinatorics_en.srt 4.6 KB
64 - Appendix - Working with Text Files in Python/012 Importing .csv Files - Part II_en.srt 4.6 KB
23 - Python - Variables and Data Types/002 Numbers and Boolean Values in Python_en.srt 4.6 KB
40 - Part 6 Mathematics/005 What is a Tensor_en.srt 4.6 KB
32 - Advanced Statistical Methods - Linear Regression with StatsModels/010 What is the OLS_en.srt 4.6 KB
40 - Part 6 Mathematics/004 Scalars-Vectors-and-Matrices.ipynb 4.5 KB
45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/008 Backpropagation Picture_en.srt 4.5 KB
38 - Advanced Statistical Methods - K-Means Clustering/006 Selecting-the-number-of-clusters.ipynb 4.5 KB
47 - Deep Learning - Initialization/003 State-of-the-Art Method - (Xavier) Glorot Initialization_en.srt 4.5 KB
27 - Python - Python Functions/007 Notable-Built-In-Functions-in-Python-Lecture-Py3.ipynb 4.5 KB
36 - Advanced Statistical Methods - Logistic Regression/011 Binary-Predictors-in-a-Logistic-Regression-Solution.ipynb 4.5 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/018 Underfitting and Overfitting_en.srt 4.5 KB
26 - Python - Conditional Statements/001 The IF Statement_en.srt 4.5 KB
47 - Deep Learning - Initialization/002 Types of Simple Initializations_en.srt 4.5 KB
10 - Probability - Combinatorics/004 Solving Variations with Repetition_en.srt 4.5 KB
47 - Deep Learning - Initialization/001 What is Initialization_en.srt 4.5 KB
38 - Advanced Statistical Methods - K-Means Clustering/014 Species-Segmentation-with-Cluster-Analysis-Part-1-Exercise.ipynb 4.5 KB
36 - Advanced Statistical Methods - Logistic Regression/005 Building-a-Logistic-Regression-Solution.ipynb 4.4 KB
22 - Part 4 Introduction to Python/005 Understanding Jupyter's Interface - the Notebook Dashboard_en.srt 4.4 KB
15 - Statistics - Descriptive Statistics/013 Skewness_en.srt 4.4 KB
42 - Deep Learning - Introduction to Neural Networks/004 The Linear Model (Linear Algebraic Version)_en.srt 4.4 KB
28 - Python - Sequences/002 Help-Yourself-with-Methods-Lecture-Py3.ipynb 4.4 KB
37 - Advanced Statistical Methods - Cluster Analysis/003 Difference between Classification and Clustering_en.srt 4.4 KB
50 - Deep Learning - Classifying on the MNIST Dataset/001 MNIST The Dataset_en.srt 4.4 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/001 What is sklearn and How is it Different from Other Packages_en.srt 4.4 KB
28 - Python - Sequences/005 Dictionaries-Lecture-Py3.ipynb 4.4 KB
50 - Deep Learning - Classifying on the MNIST Dataset/002 MNIST How to Tackle the MNIST_en.srt 4.3 KB
27 - Python - Python Functions/005 Conditional Statements and Functions_en.srt 4.3 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/023 Creating Checkpoints while Coding in Jupyter_en.srt 4.3 KB
59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/003 Selecting the Inputs for the Logistic Regression_en.srt 4.3 KB
05 - The Field of Data Science - Popular Data Science Techniques/008 Real Life Examples of Traditional Methods_en.srt 4.3 KB
54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/001 MNIST What is the MNIST Dataset_en.srt 4.3 KB
48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/003 Momentum_en.srt 4.3 KB
54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/005 MNIST Loss and Optimization Algorithm_en.srt 4.3 KB
28 - Python - Sequences/003 List-Slicing-Solution-Py3.ipynb 4.3 KB
24 - Python - Basic Python Syntax/001 Arithmetic-Operators-Solution-Py3.ipynb 4.2 KB
36 - Advanced Statistical Methods - Logistic Regression/004 Building a Logistic Regression_en.srt 4.2 KB
64 - Appendix - Working with Text Files in Python/017 Importing-Text-Data-DSc-Exercise.ipynb 4.2 KB
44 - Deep Learning - TensorFlow 2.0 Introduction/005 Types of File Formats Supporting TensorFlow_en.srt 4.1 KB
11 - Probability - Bayesian Inference/006 Dependence and Independence of Sets_en.srt 4.1 KB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/001 Multiple Linear Regression_en.srt 4.1 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/032 Absenteeism-Exercise-EXERCISES-and-SOLUTIONS.ipynb 4.1 KB
53 - Appendix Deep Learning - TensorFlow 1 Introduction/006 Types of File Formats, supporting Tensors_en.srt 4.1 KB
48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/007 Adam (Adaptive Moment Estimation)_en.srt 4.1 KB
36 - Advanced Statistical Methods - Logistic Regression/004 Admittance-regression-tables-fixed-error.ipynb 4.1 KB
46 - Deep Learning - Overfitting/004 Training, Validation, and Test Datasets_en.srt 4.1 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/006 Simple-Linear-Regression-with-sklearn-Exercise.ipynb 4.1 KB
10 - Probability - Combinatorics/003 Simple Operations with Factorials_en.srt 4.1 KB
32 - Advanced Statistical Methods - Linear Regression with StatsModels/005 Simple-linear-regression-with-comments.ipynb 4.1 KB
53 - Appendix Deep Learning - TensorFlow 1 Introduction/002 How to Install TensorFlow 1_en.srt 4.1 KB
15 - Statistics - Descriptive Statistics/007 The Histogram_en.srt 4.1 KB
38 - Advanced Statistical Methods - K-Means Clustering/004 Clustering Categorical Data_en.srt 4.0 KB
50 - Deep Learning - Classifying on the MNIST Dataset/003 TensorFlow-MNIST-Part1-with-comments.ipynb 4.0 KB
54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/003 12.3.TensorFlow-MNIST-with-comments-Part-1.ipynb 3.9 KB
26 - Python - Conditional Statements/002 The ELSE Statement_en.srt 3.9 KB
45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/002 What is a Deep Net_en.srt 3.9 KB
18 - Statistics - Inferential Statistics Confidence Intervals/001 What are Confidence Intervals_en.srt 3.8 KB
12 - Probability - Distributions/011 Continuous Distributions The Students' T Distribution_en.srt 3.8 KB
38 - Advanced Statistical Methods - K-Means Clustering/011 Market-segmentation-example.ipynb 3.8 KB
32 - Advanced Statistical Methods - Linear Regression with StatsModels/005 Simple-linear-regression.ipynb 3.8 KB
23 - Python - Variables and Data Types/001 Variables-Solution-Py3.ipynb 3.8 KB
38 - Advanced Statistical Methods - K-Means Clustering/005 Clustering-Categorical-Data-Exercise.ipynb 3.8 KB
36 - Advanced Statistical Methods - Logistic Regression/006 An Invaluable Coding Tip_en.srt 3.8 KB
26 - Python - Conditional Statements/004 A Note on Boolean Values_en.srt 3.7 KB
55 - Appendix Deep Learning - TensorFlow 1 Business Case/009 Business Case Interpretation_en.srt 3.7 KB
27 - Python - Python Functions/003 Defining a Function in Python - Part II_en.srt 3.7 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/012 Creating a Summary Table with P-values_en.srt 3.7 KB
27 - Python - Python Functions/007 Notable-Built-In-Functions-in-Python-Exercise-Py3.ipynb 3.7 KB
43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/002 Minimal-example-Part-2.ipynb 3.7 KB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/005 OLS Assumptions_en.srt 3.6 KB
36 - Advanced Statistical Methods - Logistic Regression/012 Accuracy.ipynb 3.6 KB
38 - Advanced Statistical Methods - K-Means Clustering/015 iris-with-answers.csv 3.6 KB
50 - Deep Learning - Classifying on the MNIST Dataset/003 MNIST Importing the Relevant Packages and Loading the Data_en.srt 3.6 KB
38 - Advanced Statistical Methods - K-Means Clustering/003 A-Simple-Example-of-Clustering-Exercise.ipynb 3.6 KB
23 - Python - Variables and Data Types/001 Variables-Lecture-Py3.ipynb 3.6 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/002 How are we Going to Approach this Section_en.srt 3.6 KB
40 - Part 6 Mathematics/010 Dot-product-Part-2.ipynb 3.6 KB
50 - Deep Learning - Classifying on the MNIST Dataset/009 MNIST Select the Loss and the Optimizer_en.srt 3.6 KB
05 - The Field of Data Science - Popular Data Science Techniques/011 Real Life Examples of Machine Learning (ML)_en.srt 3.6 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/006 Using a Statistical Approach towards the Solution to the Exercise_en.srt 3.6 KB
32 - Advanced Statistical Methods - Linear Regression with StatsModels/006 Simple-Linear-Regression-Exercise-Solution.ipynb 3.6 KB
48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/002 Problems with Gradient Descent_en.srt 3.6 KB
36 - Advanced Statistical Methods - Logistic Regression/002 Admittance.ipynb 3.5 KB
12 - Probability - Distributions/012 Continuous Distributions The Chi-Squared Distribution_en.srt 3.5 KB
24 - Python - Basic Python Syntax/001 Arithmetic-Operators-Lecture-Py3.ipynb 3.5 KB
42 - Deep Learning - Introduction to Neural Networks/009 Common Objective Functions L2-norm Loss_en.srt 3.5 KB
12 - Probability - Distributions/004 Discrete Distributions The Uniform Distribution_en.srt 3.4 KB
25 - Python - Other Python Operators/002 Logical-and-Identity-Operators-Solution-Py3.ipynb 3.4 KB
46 - Deep Learning - Overfitting/002 Underfitting and Overfitting for Classification_en.srt 3.4 KB
49 - Deep Learning - Preprocessing/004 Preprocessing Categorical Data_en.srt 3.4 KB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/012 real-estate-price-size-year-view.csv 3.4 KB
54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/007 MNIST Batching and Early Stopping_en.srt 3.4 KB
23 - Python - Variables and Data Types/002 Numbers-and-Boolean-Values-Lecture-Py3.ipynb 3.4 KB
53 - Appendix Deep Learning - TensorFlow 1 Introduction/006 5.3.TensorFlow-Minimal-example-Part-1.ipynb 3.4 KB
64 - Appendix - Working with Text Files in Python/020 Working with Excel (.xlsx) Data_en.srt 3.4 KB
42 - Deep Learning - Introduction to Neural Networks/005 The Linear Model with Multiple Inputs_en.srt 3.3 KB
38 - Advanced Statistical Methods - K-Means Clustering/004 Categorical-data.ipynb 3.3 KB
38 - Advanced Statistical Methods - K-Means Clustering/002 Country-clusters.ipynb 3.3 KB
27 - Python - Python Functions/003 Another-Way-to-Define-a-Function-Lecture-Py3.ipynb 3.3 KB
11 - Probability - Bayesian Inference/005 Mutually Exclusive Sets_en.srt 3.3 KB
26 - Python - Conditional Statements/003 Else-If-for-Brief-Elif-Lecture-Py3.ipynb 3.2 KB
40 - Part 6 Mathematics/006 Adding-and-subtracting-matrices.ipynb 3.2 KB
55 - Appendix Deep Learning - TensorFlow 1 Business Case/010 Business Case Testing the Model_en.srt 3.2 KB
42 - Deep Learning - Introduction to Neural Networks/007 Graphical Representation of Simple Neural Networks_en.srt 3.2 KB
40 - Part 6 Mathematics/007 Errors when Adding Matrices_en.srt 3.2 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/032 Final Remarks of this Section_en.srt 3.2 KB
52 - Deep Learning - Conclusion/002 What's Further out there in terms of Machine Learning_en.srt 3.2 KB
45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/001 What is a Layer_en.srt 3.2 KB
28 - Python - Sequences/001 Lists-Solution-Py3.ipynb 3.2 KB
64 - Appendix - Working with Text Files in Python/025 Saving-Data-NP-Template.ipynb 3.2 KB
40 - Part 6 Mathematics/007 Errors-when-adding-scalars-vectors-and-matrices-in-Python.ipynb 3.2 KB
36 - Advanced Statistical Methods - Logistic Regression/008 Understanding-Logistic-Regression-Tables-Exercise.ipynb 3.2 KB
27 - Python - Python Functions/001 Defining a Function in Python_en.srt 3.2 KB
11 - Probability - Bayesian Inference/009 The Additive Rule_en.srt 3.1 KB
55 - Appendix Deep Learning - TensorFlow 1 Business Case/002 Business Case Outlining the Solution_en.srt 3.1 KB
65 - Bonus Lecture/001 Bonus Lecture Next Steps.html 3.1 KB
25 - Python - Other Python Operators/001 Comparison Operators_en.srt 3.1 KB
24 - Python - Basic Python Syntax/003 Reassign-Values-Lecture-Py3.ipynb 3.1 KB
11 - Probability - Bayesian Inference/003 Intersection of Sets_en.srt 3.1 KB
12 - Probability - Distributions/003 Characteristics of Discrete Distributions_en.srt 3.0 KB
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29 - Python - Iterations/005 Conditional Statements, Functions, and Loops_en.srt 3.0 KB
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33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/004 Test for Significance of the Model (F-Test)_en.srt 2.9 KB
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36 - Advanced Statistical Methods - Logistic Regression/005 Building-a-Logistic-Regression-Exercise.ipynb 2.9 KB
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40 - Part 6 Mathematics/008 Tranpose-of-a-matrix.ipynb 2.9 KB
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28 - Python - Sequences/002 Help-Yourself-with-Methods-Solution-Py3.ipynb 2.8 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/005 What's Regression Analysis - a Quick Refresher.html 2.8 KB
33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/002 Multiple-linear-regression-and-Adjusted-R-squared-with-comments.ipynb 2.8 KB
05 - The Field of Data Science - Popular Data Science Techniques/002 Real Life Examples of Traditional Data_en.srt 2.8 KB
28 - Python - Sequences/003 List-Slicing-Exercise-Py3.ipynb 2.8 KB
53 - Appendix Deep Learning - TensorFlow 1 Introduction/005 Actual Introduction to TensorFlow_en.srt 2.8 KB
31 - Part 5 Advanced Statistical Methods in Python/001 Introduction to Regression Analysis_en.srt 2.8 KB
32 - Advanced Statistical Methods - Linear Regression with StatsModels/006 Simple-Linear-Regression-Exercise.ipynb 2.8 KB
24 - Python - Basic Python Syntax/007 Structuring with Indentation_en.srt 2.8 KB
38 - Advanced Statistical Methods - K-Means Clustering/010 Relationship between Clustering and Regression_en.srt 2.7 KB
64 - Appendix - Working with Text Files in Python/007 Text Files of Fixed Width_en.srt 2.7 KB
42 - Deep Learning - Introduction to Neural Networks/008 What is the Objective Function_en.srt 2.7 KB
28 - Python - Sequences/001 Lists-Lecture-Py3.ipynb 2.7 KB
05 - The Field of Data Science - Popular Data Science Techniques/006 Real Life Examples of Business Intelligence (BI)_en.srt 2.7 KB
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48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/temp/index_13061118.m3u8 2.6 KB
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23 - Python - Variables and Data Types/003 Strings-Exercise-Py3.ipynb 2.6 KB
32 - Advanced Statistical Methods - Linear Regression with StatsModels/002 Correlation vs Regression_en.srt 2.6 KB
54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/003 MNIST Relevant Packages_en.srt 2.6 KB
36 - Advanced Statistical Methods - Logistic Regression/010 2.02.Binary-predictors.csv 2.6 KB
51 - Deep Learning - Business Case Example/011 Business Case Testing the Model_en.srt 2.6 KB
36 - Advanced Statistical Methods - Logistic Regression/011 Binary-Predictors-in-a-Logistic-Regression-Exercise.ipynb 2.5 KB
25 - Python - Other Python Operators/001 Comparison-Operators-Lecture-Py3.ipynb 2.5 KB
27 - Python - Python Functions/004 How to Use a Function within a Function_en.srt 2.5 KB
17 - Statistics - Inferential Statistics Fundamentals/007 Standard error_en.srt 2.5 KB
36 - Advanced Statistical Methods - Logistic Regression/004 Admittance-regression-summary-error.ipynb 2.5 KB
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33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/003 Multiple-Linear-Regression-Exercise.ipynb 2.5 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/001 What to Expect from the Following Sections.html 2.4 KB
18 - Statistics - Inferential Statistics Confidence Intervals/015 Confidence intervals. Two means. Independent Samples (Part 3)_en.srt 2.4 KB
36 - Advanced Statistical Methods - Logistic Regression/010 Binary-predictors.ipynb 2.4 KB
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33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/003 real-estate-price-size-year.csv 2.4 KB
34 - Advanced Statistical Methods - Linear Regression with sklearn/013 real-estate-price-size-year.csv 2.4 KB
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24 - Python - Basic Python Syntax/004 Add Comments_en.srt 2.3 KB
58 - Case Study - Preprocessing the 'Absenteeism_data'/014 Dropping a Dummy Variable from the Data Set.html 2.3 KB
24 - Python - Basic Python Syntax/002 The Double Equality Sign_en.srt 2.3 KB
23 - Python - Variables and Data Types/002 Numbers-and-Boolean-Values-Exercise-Py3.ipynb 2.3 KB
53 - Appendix Deep Learning - TensorFlow 1 Introduction/003 A Note on Installing Packages in Anaconda.html 2.3 KB
64 - Appendix - Working with Text Files in Python/015 Importing-Text-Data-with-NumPy-Template.ipynb 2.3 KB
51 - Deep Learning - Business Case Example/002 Business Case Outlining the Solution_en.srt 2.3 KB
29 - Python - Iterations/003 Create-Lists-with-the-range-Function-Solution-Py3.ipynb 2.3 KB
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