[FreeCourseSite.com] Udemy - Machine Learning in Python with 5 Machine Learning Projects

Creation Time	Nov. 11, 2023, 12:34 p.m.
Last Access Time	Dec. 19, 2024, 5:44 p.m.
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Keywords	FreeCourseSite Python with - Udemy Machine 5 Learning in com Projects
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Total Files	825
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12. Tree Based Models/2. Attribute selection method- Gini Index and Entropy.mp4 218.7 MB
11. Introduction to KNN, SVM, Naive Bayes/6. Introduction to Naive Bayes.mp4 174.7 MB
13. Boosting Models/2. Intuition for Adaboost and Gradient Boosting.mp4 153.3 MB
10. Logistic Regression/8. Using ROC-AUC score to analyze the performance of model.mp4 147.6 MB
10. Logistic Regression/6. How to analyze performance of a classification model.mp4 146.2 MB
13. Boosting Models/7. Introudction to Ensembling techniques.mp4 134.0 MB
20. Predicting Health Expense of Customers/8. Applying Linear Regression Model.mp4 128.1 MB
2. Python for Data Analysis/17. Time Complexity.mp4 120.1 MB
2. Python for Data Analysis/21. Insertion and Selection Sort.mp4 120.0 MB
1. Python Fundamentals/4. Built in Data Types and Type Casting.mp4 119.9 MB
18. Time Series Forecasting/8. Handling Missing Values.mp4 116.5 MB
2. Python for Data Analysis/22. Merge Sort.mp4 115.4 MB
17. Recommendation Engines/19. Introduction to SVD.mp4 112.0 MB
2. Python for Data Analysis/19. Binary Search.mp4 109.5 MB
9. Linear Regression/6. Analyzing the performance of Regression models.mp4 109.0 MB
11. Introduction to KNN, SVM, Naive Bayes/1. Introduction to Support Vector machines.mp4 108.2 MB
9. Linear Regression/9. Applying real time prediction on our model.mp4 107.6 MB
9. Linear Regression/7. R2 score and adjuted R2 score intuition.mp4 107.0 MB
10. Logistic Regression/1. Introduction to Logistic Regression.mp4 106.4 MB
5. Data Cleaning/24. Data Cleaning on Naukri Dataset.mp4 106.2 MB
9. Linear Regression/5. Applying Cross Validation.mp4 105.6 MB
11. Introduction to KNN, SVM, Naive Bayes/4. Introduction to K nearest neighbors.mp4 104.3 MB
20. Predicting Health Expense of Customers/2. Understanding the Dataset.mp4 104.0 MB
1. Python Fundamentals/3. Naming Convention for Variables.mp4 102.2 MB
16. Dimensionality Reduction/3. Solving a Real World Problem.mp4 98.8 MB
15. Introduction to Clustering Analysis/9. Using Silhouette Score to analyze the clusters.mp4 96.3 MB
19. Employee Promotion Prediction/2. Understanding the Dataset.mp4 95.9 MB
2. Python for Data Analysis/18. Linear Search.mp4 95.5 MB
20. Predicting Health Expense of Customers/7. Preparing the data for Modelling.mp4 90.9 MB
13. Boosting Models/3. Implementing AdaBoost using sklearn.mp4 90.8 MB
18. Time Series Forecasting/10. Time Series Decomposition.mp4 89.9 MB
20. Predicting Health Expense of Customers/4. Performing Univariate Analysis.mp4 89.8 MB
15. Introduction to Clustering Analysis/12. Introduction to Hierarchal Clustering.mp4 88.5 MB
10. Logistic Regression/2. Implementing Logistic Regression using Sklearn.mp4 87.0 MB
20. Predicting Health Expense of Customers/6. Performing Multivariate Analysis.mp4 86.0 MB
9. Linear Regression/3. Feature Selection using RFECV.mp4 85.9 MB
18. Time Series Forecasting/3. Regression Vs Time Series.mp4 82.9 MB
5. Data Cleaning/6. Imputing Missing Values in a real-time scenario.mp4 82.6 MB
12. Tree Based Models/1. Intuition for decision trees.mp4 82.0 MB
16. Dimensionality Reduction/18. Introduction to t-SNE.mp4 81.3 MB
9. Linear Regression/1. Introduction to Linear Regression.mp4 81.2 MB
17. Recommendation Engines/12. Introduction to Collaborative Filtering.mp4 80.9 MB
3. Python Functions Deep Dive/9. Filter, Map, and Zip Functions.mp4 79.9 MB
5. Data Cleaning/3. When should we delete the Missing values.mp4 79.6 MB
16. Dimensionality Reduction/1. Why High Dimensional Datasets are a Problem.mp4 79.2 MB
18. Time Series Forecasting/14. Metrics for Time series Forecasting.mp4 78.7 MB
1. Python Fundamentals/8. Arithmetic and Assignment Operators.mp4 78.0 MB
19. Employee Promotion Prediction/15. Performance Analysis.mp4 77.2 MB
1. Python Fundamentals/5. Scope of Variables.mp4 77.2 MB
18. Time Series Forecasting/25. Auto Correlation and Partial Correlation.mp4 76.8 MB
17. Recommendation Engines/6. Preprocessing the Data for Content Based Filtering.mp4 76.7 MB
4. Python for Data Science/21. Merging and Concatenating DataFrames.mp4 76.6 MB
2. Python for Data Analysis/20. Bubble Sort.mp4 75.5 MB
2. Python for Data Analysis/8. Introduction to Sets.mp4 75.5 MB
18. Time Series Forecasting/21. Checking for Stationarity using Statistical Methods Part 2.mp4 75.4 MB
21. Determining Whether a Person should be Granted Loan/4. Performing Descriptive Statistics.mp4 75.3 MB
10. Logistic Regression/9. Real time prediction using logistic regression.mp4 74.7 MB
4. Python for Data Science/7. Meta Characters for Regular Expressions.mp4 74.0 MB
5. Data Cleaning/4. Imputing the Missing Values using the Business Logic.mp4 73.9 MB
16. Dimensionality Reduction/16. Introduction to Principal Component Analysis.mp4 73.8 MB
22. Optimizing Agricultural Production/4. Performing Descriptive Statistics.mp4 73.6 MB
9. Linear Regression/2. Implementing Linear Regression using Sklearn.mp4 73.5 MB
2. Python for Data Analysis/24. Quiz Solution.mp4 73.2 MB
18. Time Series Forecasting/17. Holt and Holt Winter Exponential Smoothing.mp4 73.1 MB
7. Feature Engineering/9. Finding the Length, Polarity and Subjectivity.mp4 73.0 MB
17. Recommendation Engines/13. Preprocessing the Data for Collaborative Filtering.mp4 72.4 MB
15. Introduction to Clustering Analysis/8. Implementing K Means on the Mall Dataset.mp4 71.6 MB
20. Predicting Health Expense of Customers/5. Performing Bivariate Analysis.mp4 71.5 MB
16. Dimensionality Reduction/5. Introduction to Correlation using Heatmap.mp4 71.4 MB
14. Imbalanced Machine Learning/5. Applying Logistic Regression using Sklearn.mp4 71.1 MB
18. Time Series Forecasting/7. Getting Time Series data.mp4 71.1 MB
15. Introduction to Clustering Analysis/7. Solving a Real World Problem.mp4 71.0 MB
6. Data Visualizations/3. Multivariate Analysis.mp4 70.8 MB
14. Imbalanced Machine Learning/2. Using Resampling Techniques to Balance the Data.mp4 70.6 MB
20. Predicting Health Expense of Customers/10. Applying Gradient Boosting Model.mp4 70.4 MB
11. Introduction to KNN, SVM, Naive Bayes/2. The kermel trick for support vector machine.mp4 70.4 MB
21. Determining Whether a Person should be Granted Loan/7. Bivariate Data Analysis.mp4 70.2 MB
18. Time Series Forecasting/36. Understanding SARIMA Model.mp4 69.9 MB
11. Introduction to KNN, SVM, Naive Bayes/8. When should we apply SVM, KNN and Naive bayes.mp4 69.8 MB
5. Data Cleaning/9. How Outliers can be harmful for Machine Learning Models.mp4 69.0 MB
4. Python for Data Science/20. Indexing, Selecting, and Filtering Data using Pandas.mp4 68.9 MB
21. Determining Whether a Person should be Granted Loan/2. Setting up the Environment.mp4 68.6 MB
12. Tree Based Models/6. Introduction to Random forest.mp4 68.1 MB
19. Employee Promotion Prediction/12. Data Processing.mp4 67.7 MB
11. Introduction to KNN, SVM, Naive Bayes/3. Implementing support vector machine using sklearn.mp4 67.4 MB
15. Introduction to Clustering Analysis/5. Using the Elbow Method for Choosing the Best Value for K.mp4 67.1 MB
21. Determining Whether a Person should be Granted Loan/5. Data Cleaning.mp4 67.0 MB
13. Boosting Models/4. Implementing Gradient Boosting using sklearn.mp4 66.9 MB
2. Python for Data Analysis/6. Introduction to Dictionaries.mp4 66.8 MB
18. Time Series Forecasting/16. Simple Exponential Smoothing.mp4 66.6 MB
18. Time Series Forecasting/39. Understanding ARIMAX Model.mp4 66.5 MB
12. Tree Based Models/5. Understanding the concept of Bagging.mp4 66.0 MB
1. Python Fundamentals/18. If, elif, and else.mp4 65.9 MB
4. Python for Data Science/18. Reading Datasets using Pandas.mp4 65.8 MB
1. Python Fundamentals/1. Why should you learn Python.mp4 65.7 MB
15. Introduction to Clustering Analysis/2. Types of Clustering.mp4 65.2 MB
21. Determining Whether a Person should be Granted Loan/6. Univariate Data Visualizations.mp4 65.2 MB
13. Boosting Models/6. Implementing XGBoost using sklearn.mp4 65.1 MB
18. Time Series Forecasting/20. Checking for Stationarity Part 1.mp4 65.0 MB
18. Time Series Forecasting/28. The Simple Auto Regressive Model Implementation.mp4 65.0 MB
7. Feature Engineering/26. Quiz Solution.mp4 64.8 MB
16. Dimensionality Reduction/22. Difference between PCA, t-SNE, and LDA.mp4 64.8 MB
18. Time Series Forecasting/9. Handling Outlier Values.mp4 64.4 MB
5. Data Cleaning/1. Causes and Impact of Missing Values.mp4 64.4 MB
14. Imbalanced Machine Learning/11. Implementing Neighbors based Sampling using Imblearn.mp4 64.0 MB
22. Optimizing Agricultural Production/6. Clustering Similar Crops.mp4 63.6 MB
17. Recommendation Engines/15. Interpreting the Results obtained from User Based Filtering.mp4 63.6 MB
17. Recommendation Engines/16. Implementation of Item Based Collaborative Filtering.mp4 63.6 MB
18. Time Series Forecasting/11. Splitting Time Series Data.mp4 63.5 MB
18. Time Series Forecasting/27. The Simple Auto Regressive Model.mp4 63.4 MB
17. Recommendation Engines/8. Introduction to Transactional Encoder.mp4 63.4 MB
5. Data Cleaning/10. Finding out Outliers from the Data.mp4 63.2 MB
3. Python Functions Deep Dive/18. Solving the Fibonacci Problem using Recursion.mp4 62.7 MB
1. Python Fundamentals/9. Comparison, Logical, and Bitwise Operators.mp4 62.4 MB
3. Python Functions Deep Dive/23. Encapsulation.mp4 62.2 MB
17. Recommendation Engines/14. Implementation of User Based Collaborative Filtering.mp4 62.1 MB
17. Recommendation Engines/22. Comparing Content, and Collaborative Based Filtering.mp4 62.0 MB
11. Introduction to KNN, SVM, Naive Bayes/7. Implementing Naive Bayes using sklearn.mp4 62.0 MB
5. Data Cleaning/2. Types of Missing Values.mp4 61.8 MB
20. Predicting Health Expense of Customers/3. Understanding the Problem Statement.mp4 61.8 MB
19. Employee Promotion Prediction/4. Performing Descriptive Statistics.mp4 61.7 MB
8. Data Processing/1. Types of Encoding Techniques.mp4 60.9 MB
5. Data Cleaning/12. Deleting and Capping the Outliers.mp4 60.8 MB
2. Python for Data Analysis/7. Nested Dictionaries.mp4 60.5 MB
7. Feature Engineering/1. Introduction to Feature Engineering.mp4 60.0 MB
18. Time Series Forecasting/42. Implementing SARIMAX Model.mp4 60.0 MB
10. Logistic Regression/10. Industry Relevance of Logistic Regression.mp4 59.9 MB
19. Employee Promotion Prediction/3. Understanding the Problem Statement.mp4 59.8 MB
7. Feature Engineering/5. Binning Numerical Features.mp4 59.4 MB
17. Recommendation Engines/5. Introduction to Content Based Filtering.mp4 59.0 MB
10. Logistic Regression/4. Hyperparameter tuning using Grid search.mp4 58.8 MB
17. Recommendation Engines/7. Filtering Movies Based on Genres.mp4 58.7 MB
7. Feature Engineering/23. Feature Engineering on Marketing Data.mp4 58.6 MB
2. Python for Data Analysis/9. Set Operations.mp4 58.6 MB
17. Recommendation Engines/26. Case Study for Youtube.mp4 58.1 MB
14. Imbalanced Machine Learning/4. Preparing the Data for Predictive Modelling.mp4 57.9 MB
15. Introduction to Clustering Analysis/1. Introduction to Clustering.mp4 57.8 MB
20. Predicting Health Expense of Customers/14. Major Takeaways from the Project.mp4 57.6 MB
4. Python for Data Science/17. Quiz Solution.mp4 57.6 MB
14. Imbalanced Machine Learning/9. Implementing Random Under Sampling using Imblearn.mp4 57.5 MB
9. Linear Regression/4. Data Transformation with Linear Regression.mp4 57.5 MB
14. Imbalanced Machine Learning/10. Implementing Synthetic Sampling using Imblearn.mp4 57.4 MB
16. Dimensionality Reduction/2. Methods to solve the problem of High Dimensionality.mp4 57.2 MB
13. Boosting Models/1. Understading the concept of boosting.mp4 57.1 MB
7. Feature Engineering/20. Feature Engineering on Employee Data.mp4 57.1 MB
20. Predicting Health Expense of Customers/11. Creating Ensembles of Models.mp4 57.1 MB
6. Data Visualizations/1. Univariate Analysis.mp4 57.1 MB
14. Imbalanced Machine Learning/3. Solving a Real World Problem.mp4 57.0 MB
21. Determining Whether a Person should be Granted Loan/9. Applying Resampling.mp4 57.0 MB
7. Feature Engineering/6. Aggregating Features.mp4 56.9 MB
7. Feature Engineering/2. Removing Unnecessary Columns.mp4 56.9 MB
18. Time Series Forecasting/32. Understanding ARMA Model.mp4 56.8 MB
10. Logistic Regression/5. Applying Cross Validation.mp4 56.7 MB
16. Dimensionality Reduction/11. Introduction to Recursive Feature Selection.mp4 56.6 MB
17. Recommendation Engines/3. Types of Recommender Systems.mp4 56.5 MB
17. Recommendation Engines/25. Case Study for Netflix.mp4 56.4 MB
17. Recommendation Engines/9. Recommending Similar Movies to Watch.mp4 56.3 MB
4. Python for Data Science/10. Sets for Regular Expressions.mp4 56.1 MB
5. Data Cleaning/15. Quiz Solution.mp4 56.1 MB
5. Data Cleaning/5. Imputing Missing Values using MeanMedianMode.mp4 56.0 MB
15. Introduction to Clustering Analysis/3. Applications of Clustering.mp4 55.9 MB
18. Time Series Forecasting/34. Understanding ARIMA Model.mp4 55.9 MB
14. Imbalanced Machine Learning/12. Combination of Oversampling and Under sampling.mp4 55.8 MB
17. Recommendation Engines/18. Quiz Solution.mp4 55.6 MB
10. Logistic Regression/7. Using accuracy score to analyze the performance of model.mp4 55.5 MB
16. Dimensionality Reduction/17. Implementing PCA.mp4 55.5 MB
18. Time Series Forecasting/13. Basic Forecasting Techniques.mp4 55.5 MB
3. Python Functions Deep Dive/17. Solving the Factorial Problem using Recursion.mp4 55.4 MB
22. Optimizing Agricultural Production/2. Understanding the Dataset.mp4 55.2 MB
14. Imbalanced Machine Learning/13. Implementing Ensemble Models for Imbalanced Data.mp4 54.9 MB
4. Python for Data Science/24. Quiz Solution.mp4 54.7 MB
3. Python Functions Deep Dive/10. List, set, and Dictionary Comprehensions.mp4 54.6 MB
14. Imbalanced Machine Learning/8. Implementing Random Over Sampling using Imblearn.mp4 54.4 MB
20. Predicting Health Expense of Customers/9. Applying Random Forest Model.mp4 54.4 MB
3. Python Functions Deep Dive/2. Default Parameters in Functions.mp4 54.0 MB
6. Data Visualizations/29. Quiz Solution.mp4 53.8 MB
12. Tree Based Models/7. Understanding the parameters of Random forest.mp4 53.7 MB
14. Imbalanced Machine Learning/1. Why Imbalanced Data needs extra attention.mp4 53.6 MB
12. Tree Based Models/3. Advantages and Issues with Decision trees.mp4 53.4 MB
17. Recommendation Engines/4. Evaluating Recommender Systems.mp4 53.2 MB
3. Python Functions Deep Dive/8. Lambda Functions.mp4 53.1 MB
19. Employee Promotion Prediction/7. Univariate Analysis.mp4 53.1 MB
1. Python Fundamentals/17. Quiz Solution.mp4 53.1 MB
1. Python Fundamentals/19. For and While.mp4 53.1 MB
18. Time Series Forecasting/50. Mean Forecast Error.mp4 52.9 MB
16. Dimensionality Reduction/13. Introduction the Boruta Algorithm.mp4 52.5 MB
21. Determining Whether a Person should be Granted Loan/10. Applying Logistic Regression.mp4 52.4 MB
15. Introduction to Clustering Analysis/15. Introduction to DBSCAN Clustering.mp4 52.4 MB
15. Introduction to Clustering Analysis/14. Implementing Hierarchial Clustering.mp4 52.4 MB
18. Time Series Forecasting/45. Choosing the Right for Model Smaller Datasets.mp4 52.3 MB
18. Time Series Forecasting/5. Components of Time Series.mp4 52.0 MB
1. Python Fundamentals/13. String Formatting.mp4 51.4 MB
7. Feature Engineering/19. Quiz Solution.mp4 51.2 MB
16. Dimensionality Reduction/12. Implementing Recursive Feature Selection.mp4 50.9 MB
4. Python for Data Science/13. Array Creation using Numpy.mp4 50.9 MB
5. Data Cleaning/13. Dealing with Outliers in a real-world scenario.mp4 50.9 MB
5. Data Cleaning/11. Using Winsorization to deal with Outliers.mp4 50.5 MB
19. Employee Promotion Prediction/10. Feature Engineering.mp4 50.4 MB
20. Predicting Health Expense of Customers/1. Setting up the Environment.mp4 50.1 MB
18. Time Series Forecasting/15. Simple Moving Averages.mp4 50.1 MB
15. Introduction to Clustering Analysis/10. Clustering Multiple Dimensions.mp4 50.0 MB
9. Linear Regression/10. Industry relevance of linear regression.mp4 49.9 MB
7. Feature Engineering/24. Feature Engineering on Titanic Data.mp4 49.6 MB
15. Introduction to Clustering Analysis/6. Introduction to K Means Clustering.mp4 49.3 MB
5. Data Cleaning/8. Quiz Solution.mp4 49.2 MB
1. Python Fundamentals/22. Quiz Solution.mp4 49.0 MB
9. Linear Regression/8. MAE, RMSE, R2 and Adjusted R2 in code.mp4 49.0 MB
16. Dimensionality Reduction/20. Introduction to Linear Discriminant Analysis.mp4 48.9 MB
16. Dimensionality Reduction/6. Removing Highly Correlated Columns using Correlation.mp4 48.9 MB
6. Data Visualizations/17. Quiz Solution.mp4 48.8 MB
2. Python for Data Analysis/12. Introduction to Stacks and Queues.mp4 48.7 MB
20. Predicting Health Expense of Customers/13. More things to Try.mp4 48.7 MB
16. Dimensionality Reduction/8. Introduction to Variance Inflation Filtering.mp4 48.7 MB
2. Python for Data Analysis/1. Differences between Lists and Tuples.mp4 48.7 MB
6. Data Visualizations/8. Bar, Line, and Area Charts.mp4 48.5 MB
17. Recommendation Engines/11. Quiz Solution.mp4 48.5 MB
18. Time Series Forecasting/33. Implementing ARMA Model.mp4 48.2 MB
18. Time Series Forecasting/24. Converting Non-Stationary Series into Stationary Implementation.mp4 48.2 MB
18. Time Series Forecasting/23. Converting Non-Stationary Series into Stationary.mp4 48.1 MB
17. Recommendation Engines/24. Quiz Solution.mp4 47.9 MB
12. Tree Based Models/8. Implementing random forest using Sklearn.mp4 47.9 MB
15. Introduction to Clustering Analysis/16. Implementing DBSCAN Clustering.mp4 47.9 MB
16. Dimensionality Reduction/9. Implementing VIF using statsmodel.mp4 47.8 MB
6. Data Visualizations/18. Animation with Bubbleplot.mp4 47.8 MB
3. Python Functions Deep Dive/7. Quiz Solution.mp4 47.7 MB
18. Time Series Forecasting/4. Applications of Time Series.mp4 47.3 MB
6. Data Visualizations/5. Quiz Solution.mp4 47.1 MB
1. Python Fundamentals/7. Quiz Solution.mp4 46.5 MB
22. Optimizing Agricultural Production/1. Setting up the Environment.mp4 46.4 MB
3. Python Functions Deep Dive/24. Polymorphism.mp4 46.3 MB
17. Recommendation Engines/21. Interpreting Results Obtained from SVD.mp4 46.0 MB
21. Determining Whether a Person should be Granted Loan/1. Understanding the Problem Statement.mp4 45.5 MB
18. Time Series Forecasting/2. Types of Forecasting.mp4 45.3 MB
6. Data Visualizations/6. Scatter Plots.mp4 45.2 MB
17. Recommendation Engines/2. What are it's Use Cases.mp4 45.1 MB
6. Data Visualizations/2. Bivariate Analysis.mp4 45.0 MB
7. Feature Engineering/21. Feature Engineering on FIFA Data.mp4 44.8 MB
18. Time Series Forecasting/40. Implementing ARIMAX Model.mp4 44.8 MB
5. Data Cleaning/16. Introduction to reindex, set_index, reset_index, and sort_index Functions.mp4 44.7 MB
19. Employee Promotion Prediction/14. Predictive Modelling.mp4 44.7 MB
2. Python for Data Analysis/2. Operations on Lists.mp4 44.4 MB
8. Data Processing/13. Train, Test and Validation Split.mp4 44.2 MB
21. Determining Whether a Person should be Granted Loan/12. Summary.mp4 44.2 MB
4. Python for Data Science/6. Quiz Solution.mp4 44.1 MB
18. Time Series Forecasting/41. Understanding SARIMAX Model.mp4 43.8 MB
14. Imbalanced Machine Learning/14. Introduction to XG Boost for Imbalanced Data.mp4 43.5 MB
1. Python Fundamentals/14. String Methods.mp4 43.3 MB
16. Dimensionality Reduction/14. Implementing the Boruta Algorithm.mp4 43.2 MB
18. Time Series Forecasting/47. Best Practices while Choosing a Time series Model..mp4 43.0 MB
21. Determining Whether a Person should be Granted Loan/8. Preparing the Data for Modelling.mp4 42.8 MB
3. Python Functions Deep Dive/5. Python Modules.mp4 42.7 MB
14. Imbalanced Machine Learning/6. Applying Random Forest using Sklearn.mp4 42.6 MB
19. Employee Promotion Prediction/6. Outlier Values Treatment.mp4 42.5 MB
19. Employee Promotion Prediction/13. Feature Scaling.mp4 42.3 MB
10. Logistic Regression/3. Feature Selection using RFECV.mp4 42.1 MB
5. Data Cleaning/23. Data Cleaning on Melbourne Housing Dataset.mp4 42.1 MB
19. Employee Promotion Prediction/16. Improvements Possible.mp4 41.9 MB
15. Introduction to Clustering Analysis/13. Introduction to Dendrograms.mp4 41.8 MB
19. Employee Promotion Prediction/1. Setting up the Environment.mp4 41.7 MB
2. Python for Data Analysis/14. Implementing Stacks and Queues using Deque.mp4 41.6 MB
14. Imbalanced Machine Learning/15. Comparing the Results.mp4 41.5 MB
5. Data Cleaning/20. Introduction to Melt, Explode, and Squeeze Functions.mp4 41.4 MB
21. Determining Whether a Person should be Granted Loan/3. Understanding the Dataset.mp4 41.1 MB
13. Boosting Models/5. Getting High level intuition for XGBoost.mp4 41.1 MB
1. Python Fundamentals/15. User Input.mp4 41.0 MB
4. Python for Data Science/9. Special Characters for Regular Expressions.mp4 40.9 MB
1. Python Fundamentals/20. Break and Continue.mp4 40.7 MB
17. Recommendation Engines/20. Implementing SVD using Surprise.mp4 40.6 MB
17. Recommendation Engines/1. Introduction to Recommender systems.mp4 40.5 MB
3. Python Functions Deep Dive/26. Quiz Solution.mp4 40.5 MB
22. Optimizing Agricultural Production/10. Summarizing the Key-Points.mp4 40.5 MB
22. Optimizing Agricultural Production/8. Predictive Modelling.mp4 40.4 MB
3. Python Functions Deep Dive/12. Quiz Solution.mp4 40.3 MB
3. Python Functions Deep Dive/1. Introduction to Functions.mp4 40.2 MB
4. Python for Data Science/15. Built-in Functions in Numpy.mp4 40.0 MB
19. Employee Promotion Prediction/9. Multivariate Analysis.mp4 39.9 MB
6. Data Visualizations/36. Network Charts.mp4 39.7 MB
8. Data Processing/14. Standardization and Normalization.mp4 39.7 MB
3. Python Functions Deep Dive/21. Introduction to Classes and Objects.mp4 39.5 MB
2. Python for Data Analysis/16. Quiz Solution.mp4 39.5 MB
8. Data Processing/9. Square and Cube Root Transformation.mp4 39.4 MB
1. Python Fundamentals/10. Identity and Membership Operators.mp4 39.2 MB
22. Optimizing Agricultural Production/5. Analyzing Agricultural Conditions.mp4 39.2 MB
6. Data Visualizations/32. Funnel Charts.mp4 39.1 MB
19. Employee Promotion Prediction/5. Missing Values Treatment.mp4 38.7 MB
21. Determining Whether a Person should be Granted Loan/11. Applying Gradient Boosting.mp4 38.6 MB
6. Data Visualizations/24. Introduction to Ipywidgets.mp4 38.6 MB
6. Data Visualizations/38. Quiz Solution.mp4 38.5 MB
18. Time Series Forecasting/26. Auto Correlation and Partial Correlation Implementation.mp4 38.5 MB
6. Data Visualizations/10. Statistical Charts.mp4 38.4 MB
5. Data Cleaning/21. Data Cleaning on Big Mart Dataset.mp4 38.3 MB
7. Feature Engineering/3. Decomposing Time and Date Features.mp4 38.3 MB
7. Feature Engineering/4. Decomposing Categorical Features.mp4 38.3 MB
2. Python for Data Analysis/11. Quiz Solution.mp4 38.3 MB
3. Python Functions Deep Dive/16. Quiz Solution.mp4 38.2 MB
18. Time Series Forecasting/37. Implementing SARIMA Model.mp4 38.1 MB
18. Time Series Forecasting/22. Checking for Stationary Implementation.mp4 38.1 MB
3. Python Functions Deep Dive/20. Quiz Solution.mp4 38.1 MB
6. Data Visualizations/9. Facet Grids.mp4 37.9 MB
5. Data Cleaning/18. Introduction to Split and Strip Function.mp4 37.8 MB
4. Python for Data Science/8. Built-in Functions for Regular Expressions.mp4 37.6 MB
8. Data Processing/8. Introduction to Skewness and Normal Distribution.mp4 37.6 MB
4. Python for Data Science/1. Introduction to datetime.mp4 37.5 MB
19. Employee Promotion Prediction/11. Categorical Encoding.mp4 37.4 MB
5. Data Cleaning/22. Data Cleaning on Movie Dataset.mp4 37.3 MB
4. Python for Data Science/22. Lambda, Map, and Apply Functions.mp4 37.2 MB
19. Employee Promotion Prediction/8. Bivariate Analysis.mp4 37.2 MB
2. Python for Data Analysis/5. Quiz Solution.mp4 37.1 MB
16. Dimensionality Reduction/21. Implementing LDA.mp4 36.7 MB
20. Predicting Health Expense of Customers/12. Comparing Performance of these Models.mp4 36.5 MB
2. Python for Data Analysis/13. Implementing Stacks and Queues using Lists.mp4 36.5 MB
4. Python for Data Science/14. Mathematical Operations using Numpy.mp4 36.4 MB
18. Time Series Forecasting/46. Choosing the Right Model for Larger Datasets.mp4 36.3 MB
7. Feature Engineering/10. Finding the Words, Characters, and Punctuation Count.mp4 36.3 MB
3. Python Functions Deep Dive/4. Keyword Arguments.mp4 36.2 MB
7. Feature Engineering/13. Introduction to Assign and Update Functions.mp4 36.1 MB
16. Dimensionality Reduction/19. Implementing t-SNE.mp4 36.1 MB
12. Tree Based Models/4. Implementing Decision tree using Sklearn.mp4 35.8 MB
4. Python for Data Science/19. Plotting Data in Pandas.mp4 35.7 MB
18. Time Series Forecasting/51. Mean Absolute Error.mp4 35.6 MB
22. Optimizing Agricultural Production/3. Understanding the Problem Statement.mp4 35.4 MB
7. Feature Engineering/15. Introduction to nlargest and nsmallest Functions.mp4 35.3 MB
18. Time Series Forecasting/29. Moving Average Model.mp4 35.3 MB
18. Time Series Forecasting/44. How to Choose the Right Model.mp4 35.1 MB
7. Feature Engineering/22. Feature Engineering on Hotel Reviews.mp4 35.1 MB
18. Time Series Forecasting/1. What is a Time Series Data.mp4 34.9 MB
6. Data Visualizations/12. Subplots.mp4 34.8 MB
18. Time Series Forecasting/19. Introduction to Auto Regressive Models.mp4 34.7 MB
3. Python Functions Deep Dive/14. Introduction to Analytical Functions.mp4 34.7 MB
6. Data Visualizations/23. Quiz Solution.mp4 34.6 MB
8. Data Processing/4. OneHot Encoding.mp4 34.6 MB
1. Python Fundamentals/12. Quiz Solution.mp4 34.2 MB
6. Data Visualizations/26. Interactive Bivariate Analysis.mp4 33.9 MB
7. Feature Engineering/7. Introduction to Feature Engineering on Text Data.mp4 33.8 MB
4. Python for Data Science/2. The date and time class.mp4 33.6 MB
8. Data Processing/2. Label Encoding.mp4 33.5 MB
1. Python Fundamentals/2. Installing Python and Jupyter Notebook.mp4 33.5 MB
11. Introduction to KNN, SVM, Naive Bayes/5. Implementing KNN using Sklearn.mp4 33.2 MB
8. Data Processing/5. Binary and BaseN Encoding.mp4 33.2 MB
18. Time Series Forecasting/35. Implementing ARIMA Model.mp4 33.2 MB
6. Data Visualizations/30. Sunburst Charts.mp4 33.1 MB
5. Data Cleaning/17. Introduction to Replace and Droplevel Function.mp4 33.0 MB
4. Python for Data Science/12. Quiz Solution.mp4 32.8 MB
8. Data Processing/11. BoxCox transformation.mp4 32.5 MB
3. Python Functions Deep Dive/22. Inheritance.mp4 32.5 MB
3. Python Functions Deep Dive/3. Positional Arguments.mp4 32.1 MB
6. Data Visualizations/7. Charts with Colorscale.mp4 31.8 MB
18. Time Series Forecasting/49. Why do we Evaluate Performance.mp4 31.8 MB
7. Feature Engineering/11. Counting Nouns and Verbs in the Text.mp4 31.4 MB
7. Feature Engineering/17. Introduction to Cumulative Functions.mp4 31.1 MB
6. Data Visualizations/15. Maps.mp4 30.7 MB
3. Python Functions Deep Dive/13. Introduction to Aggregate Functions.mp4 30.6 MB
6. Data Visualizations/21. Animation with Choropleth Maps.mp4 30.6 MB
7. Feature Engineering/8. Reading and Summarizing the Text.mp4 30.5 MB
7. Feature Engineering/14. Introduction to at_time and between_time Functions.mp4 30.2 MB
6. Data Visualizations/25. Interactive Univariate Analysis.mp4 29.9 MB
18. Time Series Forecasting/52. Mean Absolute Percentage Error.mp4 29.8 MB
6. Data Visualizations/14. Waffle Charts.mp4 29.4 MB
18. Time Series Forecasting/53. Root Mean Squared Error.mp4 29.3 MB
6. Data Visualizations/11. Polar Charts.mp4 29.3 MB
6. Data Visualizations/27. Interactive Multivariate Analysis.mp4 29.2 MB
8. Data Processing/3. Feature Mapping for Ordinal Variables.mp4 29.0 MB
19. Employee Promotion Prediction/17. Major Takeaways from the Project.mp4 29.0 MB
7. Feature Engineering/16. Introduction to Expanding Function.mp4 28.4 MB
8. Data Processing/10. Log transformation.mp4 28.0 MB
22. Optimizing Agricultural Production/7. Visualizing the Hidden Patterns.mp4 27.8 MB
22. Optimizing Agricultural Production/9. Real Time Predictions.mp4 27.7 MB
2. Python for Data Analysis/3. Operations on Tuples.mp4 27.4 MB
6. Data Visualizations/19. Animation with Facets.mp4 26.7 MB
5. Data Cleaning/19. Introduction to Stack, and Unstack Functions.mp4 25.4 MB
6. Data Visualizations/33. Gantt Charts.mp4 25.1 MB
6. Data Visualizations/13. 3D Charts.mp4 24.6 MB
7. Feature Engineering/12. Counting Adjectives, Adverb, and Pronouns.mp4 23.7 MB
18. Time Series Forecasting/30. Moving Average Model Implementation.mp4 23.2 MB
6. Data Visualizations/31. Parallel Co-ordinate Charts.mp4 23.0 MB
8. Data Processing/6. Mean and Frequency Encoding.mp4 22.8 MB
6. Data Visualizations/20. Animation with Scatter Maps.mp4 22.7 MB
4. Python for Data Science/3. The datetime class.mp4 22.6 MB
6. Data Visualizations/35. Tree Maps.mp4 21.5 MB
6. Data Visualizations/34. Ternary Charts.mp4 20.4 MB
4. Python for Data Science/4. The timedelta class.mp4 19.4 MB
12. Tree Based Models/2. Attribute selection method- Gini Index and Entropy.srt 13.2 KB
5. Data Cleaning/24. Data Cleaning on Naukri Dataset.srt 12.9 KB
9. Linear Regression/2. Implementing Linear Regression using Sklearn.srt 10.6 KB
11. Introduction to KNN, SVM, Naive Bayes/6. Introduction to Naive Bayes.srt 10.4 KB
9. Linear Regression/9. Applying real time prediction on our model.srt 9.6 KB
10. Logistic Regression/8. Using ROC-AUC score to analyze the performance of model.srt 9.5 KB
18. Time Series Forecasting/8. Handling Missing Values.srt 9.4 KB
13. Boosting Models/3. Implementing AdaBoost using sklearn.srt 9.4 KB
10. Logistic Regression/2. Implementing Logistic Regression using Sklearn.srt 9.2 KB
10. Logistic Regression/6. How to analyze performance of a classification model.srt 8.7 KB
13. Boosting Models/2. Intuition for Adaboost and Gradient Boosting.srt 8.6 KB
16. Dimensionality Reduction/3. Solving a Real World Problem.srt 8.4 KB
20. Predicting Health Expense of Customers/8. Applying Linear Regression Model.srt 8.1 KB
1. Python Fundamentals/8. Arithmetic and Assignment Operators.srt 8.0 KB
5. Data Cleaning/10. Finding out Outliers from the Data.srt 8.0 KB
13. Boosting Models/7. Introudction to Ensembling techniques.srt 7.8 KB
18. Time Series Forecasting/10. Time Series Decomposition.srt 7.6 KB
2. Python for Data Analysis/24. Quiz Solution.srt 7.5 KB
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11. Introduction to KNN, SVM, Naive Bayes/3. Implementing support vector machine using sklearn.srt 7.4 KB
2. Python for Data Analysis/21. Insertion and Selection Sort.srt 7.2 KB
20. Predicting Health Expense of Customers/7. Preparing the data for Modelling.srt 7.0 KB
10. Logistic Regression/9. Real time prediction using logistic regression.srt 7.0 KB
20. Predicting Health Expense of Customers/6. Performing Multivariate Analysis.srt 7.0 KB
15. Introduction to Clustering Analysis/9. Using Silhouette Score to analyze the clusters.srt 6.9 KB
10. Logistic Regression/1. Introduction to Logistic Regression.srt 6.8 KB
9. Linear Regression/4. Data Transformation with Linear Regression.srt 6.8 KB
5. Data Cleaning/6. Imputing Missing Values in a real-time scenario.srt 6.8 KB
3. Python Functions Deep Dive/9. Filter, Map, and Zip Functions.srt 6.7 KB
20. Predicting Health Expense of Customers/2. Understanding the Dataset.srt 6.6 KB
17. Recommendation Engines/6. Preprocessing the Data for Content Based Filtering.srt 6.6 KB
20. Predicting Health Expense of Customers/4. Performing Univariate Analysis.srt 6.6 KB
18. Time Series Forecasting/28. The Simple Auto Regressive Model Implementation.srt 6.5 KB
1. Python Fundamentals/4. Built in Data Types and Type Casting.srt 6.5 KB
18. Time Series Forecasting/7. Getting Time Series data.srt 6.5 KB
19. Employee Promotion Prediction/2. Understanding the Dataset.srt 6.4 KB
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22. Optimizing Agricultural Production/4. Performing Descriptive Statistics.srt 6.3 KB
9. Linear Regression/3. Feature Selection using RFECV.srt 6.3 KB
15. Introduction to Clustering Analysis/8. Implementing K Means on the Mall Dataset.srt 6.2 KB
2. Python for Data Analysis/22. Merge Sort.srt 6.2 KB
18. Time Series Forecasting/17. Holt and Holt Winter Exponential Smoothing.srt 6.1 KB
1. Python Fundamentals/3. Naming Convention for Variables.srt 6.0 KB
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21. Determining Whether a Person should be Granted Loan/4. Performing Descriptive Statistics.srt 6.0 KB
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9. Linear Regression/7. R2 score and adjuted R2 score intuition.srt 5.9 KB
17. Recommendation Engines/7. Filtering Movies Based on Genres.srt 5.8 KB
7. Feature Engineering/9. Finding the Length, Polarity and Subjectivity.srt 5.8 KB
11. Introduction to KNN, SVM, Naive Bayes/1. Introduction to Support Vector machines.srt 5.8 KB
17. Recommendation Engines/13. Preprocessing the Data for Collaborative Filtering.srt 5.7 KB
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17. Recommendation Engines/15. Interpreting the Results obtained from User Based Filtering.srt 5.6 KB
6. Data Visualizations/3. Multivariate Analysis.srt 5.5 KB
9. Linear Regression/8. MAE, RMSE, R2 and Adjusted R2 in code.srt 5.5 KB
20. Predicting Health Expense of Customers/5. Performing Bivariate Analysis.srt 5.5 KB
17. Recommendation Engines/21. Interpreting Results Obtained from SVD.srt 5.4 KB
16. Dimensionality Reduction/5. Introduction to Correlation using Heatmap.srt 5.4 KB
11. Introduction to KNN, SVM, Naive Bayes/4. Introduction to K nearest neighbors.srt 5.4 KB
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9. Linear Regression/5. Applying Cross Validation.srt 5.2 KB
2. Python for Data Analysis/11. Quiz Solution.srt 5.2 KB
19. Employee Promotion Prediction/15. Performance Analysis.srt 5.1 KB
4. Python for Data Science/7. Meta Characters for Regular Expressions.srt 5.1 KB
14. Imbalanced Machine Learning/5. Applying Logistic Regression using Sklearn.srt 5.1 KB
10. Logistic Regression/4. Hyperparameter tuning using Grid search.srt 5.0 KB
19. Employee Promotion Prediction/12. Data Processing.srt 5.0 KB
9. Linear Regression/6. Analyzing the performance of Regression models.srt 5.0 KB
4. Python for Data Science/21. Merging and Concatenating DataFrames.srt 5.0 KB
15. Introduction to Clustering Analysis/7. Solving a Real World Problem.srt 4.9 KB
21. Determining Whether a Person should be Granted Loan/5. Data Cleaning.srt 4.9 KB
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19. Employee Promotion Prediction/7. Univariate Analysis.srt 4.9 KB
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18. Time Series Forecasting/14. Metrics for Time series Forecasting.srt 4.8 KB
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14. Imbalanced Machine Learning/4. Preparing the Data for Predictive Modelling.srt 4.8 KB
13. Boosting Models/4. Implementing Gradient Boosting using sklearn.srt 4.8 KB
17. Recommendation Engines/12. Introduction to Collaborative Filtering.srt 4.8 KB
15. Introduction to Clustering Analysis/12. Introduction to Hierarchal Clustering.srt 4.8 KB
21. Determining Whether a Person should be Granted Loan/7. Bivariate Data Analysis.srt 4.8 KB
6. Data Visualizations/1. Univariate Analysis.srt 4.7 KB
21. Determining Whether a Person should be Granted Loan/2. Setting up the Environment.srt 4.7 KB
9. Linear Regression/1. Introduction to Linear Regression.srt 4.7 KB
13. Boosting Models/6. Implementing XGBoost using sklearn.srt 4.7 KB
2. Python for Data Analysis/9. Set Operations.srt 4.7 KB
20. Predicting Health Expense of Customers/10. Applying Gradient Boosting Model.srt 4.7 KB
10. Logistic Regression/7. Using accuracy score to analyze the performance of model.srt 4.6 KB
2. Python for Data Analysis/6. Introduction to Dictionaries.srt 4.6 KB
17. Recommendation Engines/14. Implementation of User Based Collaborative Filtering.srt 4.6 KB
2. Python for Data Analysis/18. Linear Search.srt 4.6 KB
5. Data Cleaning/16. Introduction to reindex, set_index, reset_index, and sort_index Functions.srt 4.6 KB
18. Time Series Forecasting/13. Basic Forecasting Techniques.srt 4.6 KB
12. Tree Based Models/1. Intuition for decision trees.srt 4.6 KB
14. Imbalanced Machine Learning/11. Implementing Neighbors based Sampling using Imblearn.srt 4.6 KB
17. Recommendation Engines/18. Quiz Solution.srt 4.6 KB
18. Time Series Forecasting/3. Regression Vs Time Series.srt 4.5 KB
20. Predicting Health Expense of Customers/11. Creating Ensembles of Models.srt 4.5 KB
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18. Time Series Forecasting/9. Handling Outlier Values.srt 4.5 KB
2. Python for Data Analysis/8. Introduction to Sets.srt 4.5 KB
4. Python for Data Science/20. Indexing, Selecting, and Filtering Data using Pandas.srt 4.5 KB
16. Dimensionality Reduction/14. Implementing the Boruta Algorithm.srt 4.5 KB
16. Dimensionality Reduction/18. Introduction to t-SNE.srt 4.5 KB
4. Python for Data Science/24. Quiz Solution.srt 4.5 KB
20. Predicting Health Expense of Customers/9. Applying Random Forest Model.srt 4.4 KB
3. Python Functions Deep Dive/2. Default Parameters in Functions.srt 4.4 KB
17. Recommendation Engines/16. Implementation of Item Based Collaborative Filtering.srt 4.4 KB
6. Data Visualizations/29. Quiz Solution.srt 4.4 KB
19. Employee Promotion Prediction/4. Performing Descriptive Statistics.srt 4.4 KB
5. Data Cleaning/15. Quiz Solution.srt 4.4 KB
17. Recommendation Engines/11. Quiz Solution.srt 4.4 KB
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7. Feature Engineering/21. Feature Engineering on FIFA Data.srt 4.4 KB
1. Python Fundamentals/17. Quiz Solution.srt 4.4 KB
12. Tree Based Models/8. Implementing random forest using Sklearn.srt 4.3 KB
21. Determining Whether a Person should be Granted Loan/6. Univariate Data Visualizations.srt 4.3 KB
16. Dimensionality Reduction/1. Why High Dimensional Datasets are a Problem.srt 4.3 KB
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16. Dimensionality Reduction/17. Implementing PCA.srt 4.3 KB
6. Data Visualizations/6. Scatter Plots.srt 4.3 KB
18. Time Series Forecasting/21. Checking for Stationarity using Statistical Methods Part 2.srt 4.3 KB
2. Python for Data Analysis/2. Operations on Lists.srt 4.3 KB
1. Python Fundamentals/22. Quiz Solution.srt 4.3 KB
12. Tree Based Models/7. Understanding the parameters of Random forest.srt 4.3 KB
14. Imbalanced Machine Learning/2. Using Resampling Techniques to Balance the Data.srt 4.3 KB
16. Dimensionality Reduction/12. Implementing Recursive Feature Selection.srt 4.2 KB
14. Imbalanced Machine Learning/8. Implementing Random Over Sampling using Imblearn.srt 4.2 KB
5. Data Cleaning/3. When should we delete the Missing values.srt 4.2 KB
6. Data Visualizations/17. Quiz Solution.srt 4.2 KB
14. Imbalanced Machine Learning/3. Solving a Real World Problem.srt 4.2 KB
8. Data Processing/1. Types of Encoding Techniques.srt 4.1 KB
16. Dimensionality Reduction/16. Introduction to Principal Component Analysis.srt 4.1 KB
22. Optimizing Agricultural Production/6. Clustering Similar Crops.srt 4.1 KB
6. Data Visualizations/38. Quiz Solution.srt 4.1 KB
7. Feature Engineering/10. Finding the Words, Characters, and Punctuation Count.srt 4.1 KB
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2. Python for Data Analysis/7. Nested Dictionaries.srt 4.1 KB
18. Time Series Forecasting/25. Auto Correlation and Partial Correlation.srt 4.1 KB
18. Time Series Forecasting/11. Splitting Time Series Data.srt 4.1 KB
3. Python Functions Deep Dive/10. List, set, and Dictionary Comprehensions.srt 4.1 KB
6. Data Visualizations/5. Quiz Solution.srt 4.0 KB
14. Imbalanced Machine Learning/10. Implementing Synthetic Sampling using Imblearn.srt 4.0 KB
18. Time Series Forecasting/16. Simple Exponential Smoothing.srt 4.0 KB
15. Introduction to Clustering Analysis/10. Clustering Multiple Dimensions.srt 4.0 KB
1. Python Fundamentals/12. Quiz Solution.srt 4.0 KB
14. Imbalanced Machine Learning/9. Implementing Random Under Sampling using Imblearn.srt 4.0 KB
17. Recommendation Engines/9. Recommending Similar Movies to Watch.srt 4.0 KB
11. Introduction to KNN, SVM, Naive Bayes/8. When should we apply SVM, KNN and Naive bayes.srt 4.0 KB
3. Python Functions Deep Dive/20. Quiz Solution.srt 4.0 KB
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12. Tree Based Models/6. Introduction to Random forest.srt 4.0 KB
5. Data Cleaning/21. Data Cleaning on Big Mart Dataset.srt 4.0 KB
16. Dimensionality Reduction/6. Removing Highly Correlated Columns using Correlation.srt 4.0 KB
5. Data Cleaning/20. Introduction to Melt, Explode, and Squeeze Functions.srt 4.0 KB
19. Employee Promotion Prediction/6. Outlier Values Treatment.srt 3.9 KB
2. Python for Data Analysis/20. Bubble Sort.srt 3.9 KB
5. Data Cleaning/18. Introduction to Split and Strip Function.srt 3.9 KB
15. Introduction to Clustering Analysis/13. Introduction to Dendrograms.srt 3.9 KB
7. Feature Engineering/6. Aggregating Features.srt 3.9 KB
5. Data Cleaning/9. How Outliers can be harmful for Machine Learning Models.srt 3.9 KB
7. Feature Engineering/19. Quiz Solution.srt 3.9 KB
3. Python Functions Deep Dive/23. Encapsulation.srt 3.9 KB
5. Data Cleaning/23. Data Cleaning on Melbourne Housing Dataset.srt 3.9 KB
15. Introduction to Clustering Analysis/2. Types of Clustering.srt 3.9 KB
18. Time Series Forecasting/24. Converting Non-Stationary Series into Stationary Implementation.srt 3.9 KB
4. Python for Data Science/6. Quiz Solution.srt 3.9 KB
5. Data Cleaning/13. Dealing with Outliers in a real-world scenario.srt 3.8 KB
4. Python for Data Science/10. Sets for Regular Expressions.srt 3.8 KB
5. Data Cleaning/4. Imputing the Missing Values using the Business Logic.srt 3.8 KB
2. Python for Data Analysis/16. Quiz Solution.srt 3.8 KB
17. Recommendation Engines/20. Implementing SVD using Surprise.srt 3.8 KB
10. Logistic Regression/5. Applying Cross Validation.srt 3.8 KB
18. Time Series Forecasting/36. Understanding SARIMA Model.srt 3.8 KB
15. Introduction to Clustering Analysis/6. Introduction to K Means Clustering.srt 3.8 KB
11. Introduction to KNN, SVM, Naive Bayes/2. The kermel trick for support vector machine.srt 3.8 KB
3. Python Functions Deep Dive/14. Introduction to Analytical Functions.srt 3.8 KB
1. Python Fundamentals/18. If, elif, and else.srt 3.7 KB
3. Python Functions Deep Dive/21. Introduction to Classes and Objects.srt 3.7 KB
18. Time Series Forecasting/15. Simple Moving Averages.srt 3.7 KB
14. Imbalanced Machine Learning/13. Implementing Ensemble Models for Imbalanced Data.srt 3.7 KB
17. Recommendation Engines/22. Comparing Content, and Collaborative Based Filtering.srt 3.7 KB
4. Python for Data Science/18. Reading Datasets using Pandas.srt 3.7 KB
18. Time Series Forecasting/50. Mean Forecast Error.srt 3.7 KB
17. Recommendation Engines/5. Introduction to Content Based Filtering.srt 3.6 KB
19. Employee Promotion Prediction/1. Setting up the Environment.srt 3.6 KB
15. Introduction to Clustering Analysis/5. Using the Elbow Method for Choosing the Best Value for K.srt 3.6 KB
2. Python for Data Analysis/5. Quiz Solution.srt 3.6 KB
18. Time Series Forecasting/23. Converting Non-Stationary Series into Stationary.srt 3.6 KB
5. Data Cleaning/1. Causes and Impact of Missing Values.srt 3.6 KB
15. Introduction to Clustering Analysis/14. Implementing Hierarchial Clustering.srt 3.6 KB
15. Introduction to Clustering Analysis/15. Introduction to DBSCAN Clustering.srt 3.6 KB
6. Data Visualizations/2. Bivariate Analysis.srt 3.6 KB
12. Tree Based Models/4. Implementing Decision tree using Sklearn.srt 3.6 KB
12. Tree Based Models/5. Understanding the concept of Bagging.srt 3.6 KB
14. Imbalanced Machine Learning/12. Combination of Oversampling and Under sampling.srt 3.6 KB
17. Recommendation Engines/24. Quiz Solution.srt 3.6 KB
20. Predicting Health Expense of Customers/1. Setting up the Environment.srt 3.6 KB
8. Data Processing/13. Train, Test and Validation Split.srt 3.6 KB
15. Introduction to Clustering Analysis/16. Implementing DBSCAN Clustering.srt 3.6 KB
16. Dimensionality Reduction/9. Implementing VIF using statsmodel.srt 3.6 KB
18. Time Series Forecasting/26. Auto Correlation and Partial Correlation Implementation.srt 3.6 KB
21. Determining Whether a Person should be Granted Loan/9. Applying Resampling.srt 3.6 KB
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7. Feature Engineering/15. Introduction to nlargest and nsmallest Functions.srt 3.6 KB
18. Time Series Forecasting/42. Implementing SARIMAX Model.srt 3.5 KB
14. Imbalanced Machine Learning/14. Introduction to XG Boost for Imbalanced Data.srt 3.5 KB
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6. Data Visualizations/26. Interactive Bivariate Analysis.srt 3.5 KB
18. Time Series Forecasting/39. Understanding ARIMAX Model.srt 3.5 KB
7. Feature Engineering/1. Introduction to Feature Engineering.srt 3.5 KB
20. Predicting Health Expense of Customers/3. Understanding the Problem Statement.srt 3.4 KB
21. Determining Whether a Person should be Granted Loan/1. Understanding the Problem Statement.srt 3.4 KB
11. Introduction to KNN, SVM, Naive Bayes/7. Implementing Naive Bayes using sklearn.srt 3.4 KB
2. Python for Data Analysis/1. Differences between Lists and Tuples.srt 3.4 KB
17. Recommendation Engines/3. Types of Recommender Systems.srt 3.4 KB
19. Employee Promotion Prediction/3. Understanding the Problem Statement.srt 3.4 KB
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6. Data Visualizations/23. Quiz Solution.srt 3.4 KB
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16. Dimensionality Reduction/2. Methods to solve the problem of High Dimensionality.srt 3.3 KB
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21. Determining Whether a Person should be Granted Loan/10. Applying Logistic Regression.srt 3.3 KB
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18. Time Series Forecasting/27. The Simple Auto Regressive Model.srt 3.2 KB
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20. Predicting Health Expense of Customers/14. Major Takeaways from the Project.srt 3.2 KB
17. Recommendation Engines/8. Introduction to Transactional Encoder.srt 3.2 KB
22. Optimizing Agricultural Production/8. Predictive Modelling.srt 3.2 KB
14. Imbalanced Machine Learning/1. Why Imbalanced Data needs extra attention.srt 3.2 KB
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15. Introduction to Clustering Analysis/1. Introduction to Clustering.srt 3.1 KB
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19. Employee Promotion Prediction/14. Predictive Modelling.srt 3.0 KB
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8. Data Processing/2. Label Encoding.srt 2.9 KB
5. Data Cleaning/5. Imputing Missing Values using MeanMedianMode.srt 2.9 KB
22. Optimizing Agricultural Production/5. Analyzing Agricultural Conditions.srt 2.9 KB
20. Predicting Health Expense of Customers/12. Comparing Performance of these Models.srt 2.9 KB
1. Python Fundamentals/15. User Input.srt 2.9 KB
6. Data Visualizations/12. Subplots.srt 2.9 KB
1. Python Fundamentals/20. Break and Continue.srt 2.9 KB
5. Data Cleaning/11. Using Winsorization to deal with Outliers.srt 2.9 KB
1. Python Fundamentals/10. Identity and Membership Operators.srt 2.8 KB
3. Python Functions Deep Dive/8. Lambda Functions.srt 2.8 KB
3. Python Functions Deep Dive/4. Keyword Arguments.srt 2.8 KB
8. Data Processing/8. Introduction to Skewness and Normal Distribution.srt 2.8 KB
6. Data Visualizations/18. Animation with Bubbleplot.srt 2.8 KB
2. Python for Data Analysis/13. Implementing Stacks and Queues using Lists.srt 2.7 KB
4. Python for Data Science/15. Built-in Functions in Numpy.srt 2.7 KB
4. Python for Data Science/9. Special Characters for Regular Expressions.srt 2.7 KB
20. Predicting Health Expense of Customers/13. More things to Try.srt 2.7 KB
9. Linear Regression/10. Industry relevance of linear regression.srt 2.7 KB
7. Feature Engineering/4. Decomposing Categorical Features.srt 2.7 KB
16. Dimensionality Reduction/21. Implementing LDA.srt 2.7 KB
16. Dimensionality Reduction/20. Introduction to Linear Discriminant Analysis.srt 2.7 KB
19. Employee Promotion Prediction/9. Multivariate Analysis.srt 2.7 KB
6. Data Visualizations/9. Facet Grids.srt 2.7 KB
2. Python for Data Analysis/14. Implementing Stacks and Queues using Deque.srt 2.7 KB
19. Employee Promotion Prediction/5. Missing Values Treatment.srt 2.7 KB
3. Python Functions Deep Dive/24. Polymorphism.srt 2.6 KB
6. Data Visualizations/11. Polar Charts.srt 2.6 KB
18. Time Series Forecasting/37. Implementing SARIMA Model.srt 2.6 KB
6. Data Visualizations/25. Interactive Univariate Analysis.srt 2.6 KB
7. Feature Engineering/11. Counting Nouns and Verbs in the Text.srt 2.6 KB
6. Data Visualizations/36. Network Charts.srt 2.6 KB
18. Time Series Forecasting/2. Types of Forecasting.srt 2.6 KB
3. Python Functions Deep Dive/22. Inheritance.srt 2.6 KB
6. Data Visualizations/15. Maps.srt 2.6 KB
4. Python for Data Science/8. Built-in Functions for Regular Expressions.srt 2.6 KB
22. Optimizing Agricultural Production/7. Visualizing the Hidden Patterns.srt 2.6 KB
4. Python for Data Science/14. Mathematical Operations using Numpy.srt 2.6 KB
3. Python Functions Deep Dive/13. Introduction to Aggregate Functions.srt 2.5 KB
18. Time Series Forecasting/4. Applications of Time Series.srt 2.5 KB
6. Data Visualizations/30. Sunburst Charts.srt 2.5 KB
5. Data Cleaning/17. Introduction to Replace and Droplevel Function.srt 2.5 KB
8. Data Processing/10. Log transformation.srt 2.5 KB
3. Python Functions Deep Dive/1. Introduction to Functions.srt 2.5 KB
18. Time Series Forecasting/51. Mean Absolute Error.srt 2.5 KB
6. Data Visualizations/33. Gantt Charts.srt 2.5 KB
19. Employee Promotion Prediction/16. Improvements Possible.srt 2.5 KB
1. Python Fundamentals/2. Installing Python and Jupyter Notebook.srt 2.4 KB
8. Data Processing/14. Standardization and Normalization.srt 2.4 KB
19. Employee Promotion Prediction/13. Feature Scaling.srt 2.4 KB
21. Determining Whether a Person should be Granted Loan/12. Summary.srt 2.4 KB
17. Recommendation Engines/2. What are it's Use Cases.srt 2.4 KB
18. Time Series Forecasting/47. Best Practices while Choosing a Time series Model..srt 2.4 KB
6. Data Visualizations/7. Charts with Colorscale.srt 2.4 KB
8. Data Processing/11. BoxCox transformation.srt 2.4 KB
7. Feature Engineering/3. Decomposing Time and Date Features.srt 2.4 KB
4. Python for Data Science/19. Plotting Data in Pandas.srt 2.4 KB
7. Feature Engineering/16. Introduction to Expanding Function.srt 2.3 KB
16. Dimensionality Reduction/19. Implementing t-SNE.srt 2.3 KB
16. Dimensionality Reduction/8. Introduction to Variance Inflation Filtering.srt 2.3 KB
2. Python for Data Analysis/3. Operations on Tuples.srt 2.3 KB
22. Optimizing Agricultural Production/10. Summarizing the Key-Points.srt 2.3 KB
18. Time Series Forecasting/35. Implementing ARIMA Model.srt 2.3 KB
18. Time Series Forecasting/41. Understanding SARIMAX Model.srt 2.3 KB
2. Python for Data Analysis/12. Introduction to Stacks and Queues.srt 2.3 KB
4. Python for Data Science/22. Lambda, Map, and Apply Functions.srt 2.3 KB
8. Data Processing/3. Feature Mapping for Ordinal Variables.srt 2.2 KB
21. Determining Whether a Person should be Granted Loan/3. Understanding the Dataset.srt 2.2 KB
3. Python Functions Deep Dive/3. Positional Arguments.srt 2.2 KB
17. Recommendation Engines/1. Introduction to Recommender systems.srt 2.2 KB
8. Data Processing/6. Mean and Frequency Encoding.srt 2.2 KB
6. Data Visualizations/27. Interactive Multivariate Analysis.srt 2.2 KB
14. Imbalanced Machine Learning/15. Comparing the Results.srt 2.1 KB
22. Optimizing Agricultural Production/9. Real Time Predictions.srt 2.1 KB
5. Data Cleaning/19. Introduction to Stack, and Unstack Functions.srt 2.1 KB
7. Feature Engineering/12. Counting Adjectives, Adverb, and Pronouns.srt 2.1 KB
13. Boosting Models/5. Getting High level intuition for XGBoost.srt 2.0 KB
11. Introduction to KNN, SVM, Naive Bayes/5. Implementing KNN using Sklearn.srt 2.0 KB
18. Time Series Forecasting/29. Moving Average Model.srt 2.0 KB
18. Time Series Forecasting/52. Mean Absolute Percentage Error.srt 2.0 KB
18. Time Series Forecasting/53. Root Mean Squared Error.srt 2.0 KB
6. Data Visualizations/21. Animation with Choropleth Maps.srt 2.0 KB
18. Time Series Forecasting/19. Introduction to Auto Regressive Models.srt 2.0 KB
18. Time Series Forecasting/1. What is a Time Series Data.srt 1.9 KB
6. Data Visualizations/13. 3D Charts.srt 1.9 KB
6. Data Visualizations/14. Waffle Charts.srt 1.9 KB
4. Python for Data Science/1. Introduction to datetime.srt 1.9 KB
22. Optimizing Agricultural Production/3. Understanding the Problem Statement.srt 1.9 KB
18. Time Series Forecasting/46. Choosing the Right Model for Larger Datasets.srt 1.9 KB
18. Time Series Forecasting/30. Moving Average Model Implementation.srt 1.8 KB
6. Data Visualizations/35. Tree Maps.srt 1.8 KB
18. Time Series Forecasting/44. How to Choose the Right Model.srt 1.8 KB
6. Data Visualizations/19. Animation with Facets.srt 1.7 KB
6. Data Visualizations/34. Ternary Charts.srt 1.7 KB
7. Feature Engineering/7. Introduction to Feature Engineering on Text Data.srt 1.7 KB
18. Time Series Forecasting/49. Why do we Evaluate Performance.srt 1.7 KB
6. Data Visualizations/31. Parallel Co-ordinate Charts.srt 1.7 KB
6. Data Visualizations/20. Animation with Scatter Maps.srt 1.5 KB
19. Employee Promotion Prediction/17. Major Takeaways from the Project.srt 1.5 KB
4. Python for Data Science/3. The datetime class.srt 1.5 KB
4. Python for Data Science/4. The timedelta class.srt 1.2 KB
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