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[Udemy] Machine Learning in Python with 5 Machine Learning Projects (04.2021)

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File Name
Size
1. Python Fundamentals/1. Why should you learn Python.mp4
66 MB
1. Python Fundamentals/1. Why should you learn Python.srt
3.3 kB
1. Python Fundamentals/10. Identity and Membership Operators.mp4
39 MB
1. Python Fundamentals/10. Identity and Membership Operators.srt
2.8 kB
1. Python Fundamentals/11. Quiz on Operators.html
147 B
1. Python Fundamentals/12. Quiz Solution.mp4
34 MB
1. Python Fundamentals/12. Quiz Solution.srt
4.0 kB
1. Python Fundamentals/13. String Formatting.mp4
51 MB
1. Python Fundamentals/13. String Formatting.srt
4.8 kB
1. Python Fundamentals/14. String Methods.mp4
43 MB
1. Python Fundamentals/14. String Methods.srt
5.9 kB
1. Python Fundamentals/15. User Input.mp4
41 MB
1. Python Fundamentals/15. User Input.srt
2.9 kB
1. Python Fundamentals/16. Quiz on Strings.html
147 B
1. Python Fundamentals/17. Quiz Solution.mp4
53 MB
1. Python Fundamentals/17. Quiz Solution.srt
4.4 kB
1. Python Fundamentals/18. If, elif, and else.mp4
66 MB
1. Python Fundamentals/18. If, elif, and else.srt
3.7 kB
1. Python Fundamentals/19. For and While.mp4
53 MB
1. Python Fundamentals/19. For and While.srt
5.3 kB
1. Python Fundamentals/2. Installing Python and Jupyter Notebook.mp4
34 MB
1. Python Fundamentals/2. Installing Python and Jupyter Notebook.srt
2.4 kB
1. Python Fundamentals/20. Break and Continue.mp4
41 MB
1. Python Fundamentals/20. Break and Continue.srt
2.9 kB
1. Python Fundamentals/21. Quiz on Loops and Conditionals.html
147 B
1. Python Fundamentals/22. Quiz Solution.mp4
49 MB
1. Python Fundamentals/22. Quiz Solution.srt
4.3 kB
1. Python Fundamentals/3. Naming Convention for Variables.mp4
102 MB
1. Python Fundamentals/3. Naming Convention for Variables.srt
6.0 kB
1. Python Fundamentals/4. Built in Data Types and Type Casting.mp4
120 MB
1. Python Fundamentals/4. Built in Data Types and Type Casting.srt
6.5 kB
1. Python Fundamentals/5. Scope of Variables.mp4
77 MB
1. Python Fundamentals/5. Scope of Variables.srt
4.1 kB
1. Python Fundamentals/6. Quiz on Variables and Data Types.html
147 B
1. Python Fundamentals/7. Quiz Solution.mp4
46 MB
1. Python Fundamentals/7. Quiz Solution.srt
5.3 kB
1. Python Fundamentals/8. Arithmetic and Assignment Operators.mp4
78 MB
1. Python Fundamentals/8. Arithmetic and Assignment Operators.srt
8.0 kB
1. Python Fundamentals/9. Comparison, Logical, and Bitwise Operators.mp4
62 MB
1. Python Fundamentals/9. Comparison, Logical, and Bitwise Operators.srt
7.5 kB
10. Logistic Regression/1. Introduction to Logistic Regression.mp4
106 MB
10. Logistic Regression/1. Introduction to Logistic Regression.srt
6.8 kB
10. Logistic Regression/10. Industry Relevance of Logistic Regression.mp4
60 MB
10. Logistic Regression/10. Industry Relevance of Logistic Regression.srt
3.3 kB
10. Logistic Regression/11. Quiz on Modelling with Logistic Regression.html
147 B
10. Logistic Regression/2. Implementing Logistic Regression using Sklearn.mp4
87 MB
10. Logistic Regression/2. Implementing Logistic Regression using Sklearn.srt
9.2 kB
10. Logistic Regression/3. Feature Selection using RFECV.mp4
42 MB
10. Logistic Regression/3. Feature Selection using RFECV.srt
3.0 kB
10. Logistic Regression/4. Hyperparameter tuning using Grid search.mp4
59 MB
10. Logistic Regression/4. Hyperparameter tuning using Grid search.srt
5.0 kB
10. Logistic Regression/5. Applying Cross Validation.mp4
57 MB
10. Logistic Regression/5. Applying Cross Validation.srt
3.8 kB
10. Logistic Regression/6. How to analyze performance of a classification model.mp4
146 MB
10. Logistic Regression/6. How to analyze performance of a classification model.srt
8.7 kB
10. Logistic Regression/7. Using accuracy score to analyze the performance of model.mp4
56 MB
10. Logistic Regression/7. Using accuracy score to analyze the performance of model.srt
4.6 kB
10. Logistic Regression/8. Using ROC-AUC score to analyze the performance of model.mp4
148 MB
10. Logistic Regression/8. Using ROC-AUC score to analyze the performance of model.srt
9.5 kB
10. Logistic Regression/9. Real time prediction using logistic regression.mp4
75 MB
10. Logistic Regression/9. Real time prediction using logistic regression.srt
7.0 kB
11. Introduction to KNN, SVM, Naive Bayes/1. Introduction to Support Vector machines.mp4
108 MB
11. Introduction to KNN, SVM, Naive Bayes/1. Introduction to Support Vector machines.srt
5.8 kB
11. Introduction to KNN, SVM, Naive Bayes/2. The kermel trick for support vector machine.mp4
70 MB
11. Introduction to KNN, SVM, Naive Bayes/2. The kermel trick for support vector machine.srt
3.8 kB
11. Introduction to KNN, SVM, Naive Bayes/3. Implementing support vector machine using sklearn.mp4
67 MB
11. Introduction to KNN, SVM, Naive Bayes/3. Implementing support vector machine using sklearn.srt
7.4 kB
11. Introduction to KNN, SVM, Naive Bayes/4. Introduction to K nearest neighbors.mp4
104 MB
11. Introduction to KNN, SVM, Naive Bayes/4. Introduction to K nearest neighbors.srt
5.4 kB
11. Introduction to KNN, SVM, Naive Bayes/5. Implementing KNN using Sklearn.mp4
33 MB
11. Introduction to KNN, SVM, Naive Bayes/5. Implementing KNN using Sklearn.srt
2.0 kB
11. Introduction to KNN, SVM, Naive Bayes/6. Introduction to Naive Bayes.mp4
175 MB
11. Introduction to KNN, SVM, Naive Bayes/6. Introduction to Naive Bayes.srt
10 kB
11. Introduction to KNN, SVM, Naive Bayes/7. Implementing Naive Bayes using sklearn.mp4
62 MB
11. Introduction to KNN, SVM, Naive Bayes/7. Implementing Naive Bayes using sklearn.srt
3.4 kB
11. Introduction to KNN, SVM, Naive Bayes/8. When should we apply SVM, KNN and Naive bayes.mp4
70 MB
11. Introduction to KNN, SVM, Naive Bayes/8. When should we apply SVM, KNN and Naive bayes.srt
4.0 kB
11. Introduction to KNN, SVM, Naive Bayes/9. Quiz on Other classification models.html
147 B
12. Tree Based Models/1. Intuition for decision trees.mp4
82 MB
12. Tree Based Models/1. Intuition for decision trees.srt
4.6 kB
12. Tree Based Models/2. Attribute selection method- Gini Index and Entropy.mp4
219 MB
12. Tree Based Models/2. Attribute selection method- Gini Index and Entropy.srt
13 kB
12. Tree Based Models/3. Advantages and Issues with Decision trees.mp4
53 MB
12. Tree Based Models/3. Advantages and Issues with Decision trees.srt
3.0 kB
12. Tree Based Models/4. Implementing Decision tree using Sklearn.mp4
36 MB
12. Tree Based Models/4. Implementing Decision tree using Sklearn.srt
3.6 kB
12. Tree Based Models/5. Understanding the concept of Bagging.mp4
66 MB
12. Tree Based Models/5. Understanding the concept of Bagging.srt
3.6 kB
12. Tree Based Models/6. Introduction to Random forest.mp4
68 MB
12. Tree Based Models/6. Introduction to Random forest.srt
4.0 kB
12. Tree Based Models/7. Understanding the parameters of Random forest.mp4
54 MB
12. Tree Based Models/7. Understanding the parameters of Random forest.srt
4.3 kB
12. Tree Based Models/8. Implementing random forest using Sklearn.mp4
48 MB
12. Tree Based Models/8. Implementing random forest using Sklearn.srt
4.3 kB
12. Tree Based Models/9. Quiz on Tree based models.html
147 B
13. Boosting Models/1. Understading the concept of boosting.mp4
57 MB
13. Boosting Models/1. Understading the concept of boosting.srt
3.0 kB
13. Boosting Models/2. Intuition for Adaboost and Gradient Boosting.mp4
153 MB
13. Boosting Models/2. Intuition for Adaboost and Gradient Boosting.srt
8.6 kB
13. Boosting Models/3. Implementing AdaBoost using sklearn.mp4
91 MB
13. Boosting Models/3. Implementing AdaBoost using sklearn.srt
9.4 kB
13. Boosting Models/4. Implementing Gradient Boosting using sklearn.mp4
67 MB
13. Boosting Models/4. Implementing Gradient Boosting using sklearn.srt
4.8 kB
13. Boosting Models/5. Getting High level intuition for XGBoost.mp4
41 MB
13. Boosting Models/5. Getting High level intuition for XGBoost.srt
2.0 kB
13. Boosting Models/6. Implementing XGBoost using sklearn.mp4
65 MB
13. Boosting Models/6. Implementing XGBoost using sklearn.srt
4.7 kB
13. Boosting Models/7. Introudction to Ensembling techniques.mp4
134 MB
13. Boosting Models/7. Introudction to Ensembling techniques.srt
7.8 kB
13. Boosting Models/8. Quiz on Boosting Models.html
147 B
14. Imbalanced Machine Learning/1. Why Imbalanced Data needs extra attention.mp4
54 MB
14. Imbalanced Machine Learning/1. Why Imbalanced Data needs extra attention.srt
3.2 kB
14. Imbalanced Machine Learning/10. Implementing Synthetic Sampling using Imblearn.mp4
57 MB
14. Imbalanced Machine Learning/10. Implementing Synthetic Sampling using Imblearn.srt
4.0 kB
14. Imbalanced Machine Learning/11. Implementing Neighbors based Sampling using Imblearn.mp4
64 MB
14. Imbalanced Machine Learning/11. Implementing Neighbors based Sampling using Imblearn.srt
4.6 kB
14. Imbalanced Machine Learning/12. Combination of Oversampling and Under sampling.mp4
56 MB
14. Imbalanced Machine Learning/12. Combination of Oversampling and Under sampling.srt
3.6 kB
14. Imbalanced Machine Learning/13. Implementing Ensemble Models for Imbalanced Data.mp4
55 MB
14. Imbalanced Machine Learning/13. Implementing Ensemble Models for Imbalanced Data.srt
3.7 kB
14. Imbalanced Machine Learning/14. Introduction to XG Boost for Imbalanced Data.mp4
44 MB
14. Imbalanced Machine Learning/14. Introduction to XG Boost for Imbalanced Data.srt
3.5 kB
14. Imbalanced Machine Learning/15. Comparing the Results.mp4
42 MB
14. Imbalanced Machine Learning/15. Comparing the Results.srt
2.1 kB
14. Imbalanced Machine Learning/16. Quiz on Handling Imbalanced Datasets.html
147 B
14. Imbalanced Machine Learning/2. Using Resampling Techniques to Balance the Data.mp4
71 MB
14. Imbalanced Machine Learning/2. Using Resampling Techniques to Balance the Data.srt
4.3 kB
14. Imbalanced Machine Learning/3. Solving a Real World Problem.mp4
57 MB
14. Imbalanced Machine Learning/3. Solving a Real World Problem.srt
4.2 kB
14. Imbalanced Machine Learning/4. Preparing the Data for Predictive Modelling.mp4
58 MB
14. Imbalanced Machine Learning/4. Preparing the Data for Predictive Modelling.srt
4.8 kB
14. Imbalanced Machine Learning/5. Applying Logistic Regression using Sklearn.mp4
71 MB
14. Imbalanced Machine Learning/5. Applying Logistic Regression using Sklearn.srt
5.1 kB
14. Imbalanced Machine Learning/6. Applying Random Forest using Sklearn.mp4
43 MB
14. Imbalanced Machine Learning/6. Applying Random Forest using Sklearn.srt
3.1 kB
14. Imbalanced Machine Learning/7. Quiz on Introduction to Imbalanced Machine Learning.html
147 B
14. Imbalanced Machine Learning/8. Implementing Random Over Sampling using Imblearn.mp4
54 MB
14. Imbalanced Machine Learning/8. Implementing Random Over Sampling using Imblearn.srt
4.2 kB
14. Imbalanced Machine Learning/9. Implementing Random Under Sampling using Imblearn.mp4
58 MB
14. Imbalanced Machine Learning/9. Implementing Random Under Sampling using Imblearn.srt
4.0 kB
15. Introduction to Clustering Analysis/1. Introduction to Clustering.mp4
58 MB
15. Introduction to Clustering Analysis/1. Introduction to Clustering.srt
3.1 kB
15. Introduction to Clustering Analysis/10. Clustering Multiple Dimensions.mp4
50 MB
15. Introduction to Clustering Analysis/10. Clustering Multiple Dimensions.srt
4.0 kB
15. Introduction to Clustering Analysis/11. Quiz on K Means Clustering.html
147 B
15. Introduction to Clustering Analysis/12. Introduction to Hierarchal Clustering.mp4
88 MB
15. Introduction to Clustering Analysis/12. Introduction to Hierarchal Clustering.srt
4.8 kB
15. Introduction to Clustering Analysis/13. Introduction to Dendrograms.mp4
42 MB
15. Introduction to Clustering Analysis/13. Introduction to Dendrograms.srt
3.9 kB
15. Introduction to Clustering Analysis/14. Implementing Hierarchial Clustering.mp4
52 MB
15. Introduction to Clustering Analysis/14. Implementing Hierarchial Clustering.srt
3.6 kB
15. Introduction to Clustering Analysis/15. Introduction to DBSCAN Clustering.mp4
52 MB
15. Introduction to Clustering Analysis/15. Introduction to DBSCAN Clustering.srt
3.6 kB
15. Introduction to Clustering Analysis/16. Implementing DBSCAN Clustering.mp4
48 MB
15. Introduction to Clustering Analysis/16. Implementing DBSCAN Clustering.srt
3.6 kB
15. Introduction to Clustering Analysis/17. Quiz on Advanced Clustering Techniques.html
147 B
15. Introduction to Clustering Analysis/2. Types of Clustering.mp4
65 MB
15. Introduction to Clustering Analysis/2. Types of Clustering.srt
3.9 kB
15. Introduction to Clustering Analysis/3. Applications of Clustering.mp4
56 MB
15. Introduction to Clustering Analysis/3. Applications of Clustering.srt
3.3 kB
15. Introduction to Clustering Analysis/4. Quiz on Introduction to Clustering.html
147 B
15. Introduction to Clustering Analysis/5. Using the Elbow Method for Choosing the Best Value for K.mp4
67 MB
15. Introduction to Clustering Analysis/5. Using the Elbow Method for Choosing the Best Value for K.srt
3.6 kB
15. Introduction to Clustering Analysis/6. Introduction to K Means Clustering.mp4
49 MB
15. Introduction to Clustering Analysis/6. Introduction to K Means Clustering.srt
3.8 kB
15. Introduction to Clustering Analysis/7. Solving a Real World Problem.mp4
71 MB
15. Introduction to Clustering Analysis/7. Solving a Real World Problem.srt
4.9 kB
15. Introduction to Clustering Analysis/8. Implementing K Means on the Mall Dataset.mp4
72 MB
15. Introduction to Clustering Analysis/8. Implementing K Means on the Mall Dataset.srt
6.2 kB
15. Introduction to Clustering Analysis/9. Using Silhouette Score to analyze the clusters.mp4
96 MB
15. Introduction to Clustering Analysis/9. Using Silhouette Score to analyze the clusters.srt
6.9 kB
16. Dimensionality Reduction/1. Why High Dimensional Datasets are a Problem.mp4
79 MB
16. Dimensionality Reduction/1. Why High Dimensional Datasets are a Problem.srt
4.3 kB
16. Dimensionality Reduction/10. Quiz on Variance Filtering.html
147 B
16. Dimensionality Reduction/11. Introduction to Recursive Feature Selection.mp4
57 MB
16. Dimensionality Reduction/11. Introduction to Recursive Feature Selection.srt
3.1 kB
16. Dimensionality Reduction/12. Implementing Recursive Feature Selection.mp4
51 MB
16. Dimensionality Reduction/12. Implementing Recursive Feature Selection.srt
4.2 kB
16. Dimensionality Reduction/13. Introduction the Boruta Algorithm.mp4
52 MB
16. Dimensionality Reduction/13. Introduction the Boruta Algorithm.srt
3.0 kB
16. Dimensionality Reduction/14. Implementing the Boruta Algorithm.mp4
43 MB
16. Dimensionality Reduction/14. Implementing the Boruta Algorithm.srt
4.5 kB
16. Dimensionality Reduction/15. Quiz on Feature Selection.html
147 B
16. Dimensionality Reduction/16. Introduction to Principal Component Analysis.mp4
74 MB
16. Dimensionality Reduction/16. Introduction to Principal Component Analysis.srt
4.1 kB
16. Dimensionality Reduction/17. Implementing PCA.mp4
56 MB
16. Dimensionality Reduction/17. Implementing PCA.srt
4.3 kB
16. Dimensionality Reduction/18. Introduction to t-SNE.mp4
81 MB
16. Dimensionality Reduction/18. Introduction to t-SNE.srt
4.5 kB
16. Dimensionality Reduction/19. Implementing t-SNE.mp4
36 MB
16. Dimensionality Reduction/19. Implementing t-SNE.srt
2.3 kB
16. Dimensionality Reduction/2. Methods to solve the problem of High Dimensionality.mp4
57 MB
16. Dimensionality Reduction/2. Methods to solve the problem of High Dimensionality.srt
3.3 kB
16. Dimensionality Reduction/20. Introduction to Linear Discriminant Analysis.mp4
49 MB
16. Dimensionality Reduction/20. Introduction to Linear Discriminant Analysis.srt
2.7 kB
16. Dimensionality Reduction/21. Implementing LDA.mp4
37 MB
16. Dimensionality Reduction/21. Implementing LDA.srt
2.7 kB
16. Dimensionality Reduction/22. Difference between PCA, t-SNE, and LDA.mp4
65 MB
16. Dimensionality Reduction/22. Difference between PCA, t-SNE, and LDA.srt
3.4 kB
16. Dimensionality Reduction/23. Quiz on Machine Learning.html
147 B
16. Dimensionality Reduction/3. Solving a Real World Problem.mp4
99 MB
16. Dimensionality Reduction/3. Solving a Real World Problem.srt
8.4 kB
16. Dimensionality Reduction/4. Quiz on Introduction.html
147 B
16. Dimensionality Reduction/5. Introduction to Correlation using Heatmap.mp4
71 MB
16. Dimensionality Reduction/5. Introduction to Correlation using Heatmap.srt
5.4 kB
16. Dimensionality Reduction/6. Removing Highly Correlated Columns using Correlation.mp4
49 MB
16. Dimensionality Reduction/6. Removing Highly Correlated Columns using Correlation.srt
4.0 kB
16. Dimensionality Reduction/7. Quiz on Correlation Filtering.html
147 B
16. Dimensionality Reduction/8. Introduction to Variance Inflation Filtering.mp4
49 MB
16. Dimensionality Reduction/8. Introduction to Variance Inflation Filtering.srt
2.3 kB
16. Dimensionality Reduction/9. Implementing VIF using statsmodel.mp4
48 MB
16. Dimensionality Reduction/9. Implementing VIF using statsmodel.srt
3.6 kB
17. Recommendation Engines/1. Introduction to Recommender systems.mp4
40 MB
17. Recommendation Engines/1. Introduction to Recommender systems.srt
2.2 kB
17. Recommendation Engines/10. Quiz on Content Based Filtering.html
147 B
17. Recommendation Engines/11. Quiz Solution.mp4
48 MB
17. Recommendation Engines/11. Quiz Solution.srt
4.4 kB
17. Recommendation Engines/12. Introduction to Collaborative Filtering.mp4
81 MB
17. Recommendation Engines/12. Introduction to Collaborative Filtering.srt
4.8 kB
17. Recommendation Engines/13. Preprocessing the Data for Collaborative Filtering.mp4
72 MB
17. Recommendation Engines/13. Preprocessing the Data for Collaborative Filtering.srt
5.7 kB
17. Recommendation Engines/14. Implementation of User Based Collaborative Filtering.mp4
62 MB
17. Recommendation Engines/14. Implementation of User Based Collaborative Filtering.srt
4.6 kB
17. Recommendation Engines/15. Interpreting the Results obtained from User Based Filtering.mp4
64 MB
17. Recommendation Engines/15. Interpreting the Results obtained from User Based Filtering.srt
5.6 kB
17. Recommendation Engines/16. Implementation of Item Based Collaborative Filtering.mp4
64 MB
17. Recommendation Engines/16. Implementation of Item Based Collaborative Filtering.srt
4.4 kB
17. Recommendation Engines/17. Quiz on Collaborative Based Filtering.html
147 B
17. Recommendation Engines/18. Quiz Solution.mp4
56 MB
17. Recommendation Engines/18. Quiz Solution.srt
4.6 kB
17. Recommendation Engines/19. Introduction to SVD.mp4
112 MB
17. Recommendation Engines/19. Introduction to SVD.srt
5.9 kB
17. Recommendation Engines/2. What are it's Use Cases.mp4
45 MB
17. Recommendation Engines/2. What are it's Use Cases.srt
2.4 kB
17. Recommendation Engines/20. Implementing SVD using Surprise.mp4
41 MB
17. Recommendation Engines/20. Implementing SVD using Surprise.srt
3.8 kB
17. Recommendation Engines/21. Interpreting Results Obtained from SVD.mp4
46 MB
17. Recommendation Engines/21. Interpreting Results Obtained from SVD.srt
5.4 kB
17. Recommendation Engines/22. Comparing Content, and Collaborative Based Filtering.mp4
62 MB
17. Recommendation Engines/22. Comparing Content, and Collaborative Based Filtering.srt
3.7 kB
17. Recommendation Engines/23. Quiz on Singular Value Decomposition.html
147 B
17. Recommendation Engines/24. Quiz Solution.mp4
48 MB
17. Recommendation Engines/24. Quiz Solution.srt
3.6 kB
17. Recommendation Engines/25. Case Study for Netflix.mp4
56 MB
17. Recommendation Engines/25. Case Study for Netflix.srt
3.0 kB
17. Recommendation Engines/26. Case Study for Youtube.mp4
58 MB
17. Recommendation Engines/26. Case Study for Youtube.srt
3.0 kB
17. Recommendation Engines/3. Types of Recommender Systems.mp4
56 MB
17. Recommendation Engines/3. Types of Recommender Systems.srt
3.4 kB
17. Recommendation Engines/4. Evaluating Recommender Systems.mp4
53 MB
17. Recommendation Engines/4. Evaluating Recommender Systems.srt
3.0 kB
17. Recommendation Engines/5. Introduction to Content Based Filtering.mp4
59 MB
17. Recommendation Engines/5. Introduction to Content Based Filtering.srt
3.6 kB
17. Recommendation Engines/6. Preprocessing the Data for Content Based Filtering.mp4
77 MB
17. Recommendation Engines/6. Preprocessing the Data for Content Based Filtering.srt
6.6 kB
17. Recommendation Engines/7. Filtering Movies Based on Genres.mp4
59 MB
17. Recommendation Engines/7. Filtering Movies Based on Genres.srt
5.8 kB
17. Recommendation Engines/8. Introduction to Transactional Encoder.mp4
63 MB
17. Recommendation Engines/8. Introduction to Transactional Encoder.srt
3.2 kB
17. Recommendation Engines/9. Recommending Similar Movies to Watch.mp4
56 MB
17. Recommendation Engines/9. Recommending Similar Movies to Watch.srt
4.0 kB
18. Time Series Forecasting/1. What is a Time Series Data.mp4
35 MB
18. Time Series Forecasting/1. What is a Time Series Data.srt
1.9 kB
18. Time Series Forecasting/10. Time Series Decomposition.mp4
90 MB
18. Time Series Forecasting/10. Time Series Decomposition.srt
7.6 kB
18. Time Series Forecasting/11. Splitting Time Series Data.mp4
64 MB
18. Time Series Forecasting/11. Splitting Time Series Data.srt
4.1 kB
18. Time Series Forecasting/12. Quiz on Time Series Analysis.html
147 B
18. Time Series Forecasting/13. Basic Forecasting Techniques.mp4
56 MB
18. Time Series Forecasting/13. Basic Forecasting Techniques.srt
4.6 kB
18. Time Series Forecasting/14. Metrics for Time series Forecasting.mp4
79 MB
18. Time Series Forecasting/14. Metrics for Time series Forecasting.srt
4.8 kB
18. Time Series Forecasting/15. Simple Moving Averages.mp4
50 MB
18. Time Series Forecasting/15. Simple Moving Averages.srt
3.7 kB
18. Time Series Forecasting/16. Simple Exponential Smoothing.mp4
67 MB
18. Time Series Forecasting/16. Simple Exponential Smoothing.srt
4.0 kB
18. Time Series Forecasting/17. Holt and Holt Winter Exponential Smoothing.mp4
73 MB
18. Time Series Forecasting/17. Holt and Holt Winter Exponential Smoothing.srt
6.1 kB
18. Time Series Forecasting/18. Quiz on Smoothing Techniques.html
147 B
18. Time Series Forecasting/19. Introduction to Auto Regressive Models.mp4
35 MB
18. Time Series Forecasting/19. Introduction to Auto Regressive Models.srt
2.0 kB
18. Time Series Forecasting/2. Types of Forecasting.mp4
45 MB
18. Time Series Forecasting/2. Types of Forecasting.srt
2.6 kB
18. Time Series Forecasting/20. Checking for Stationarity Part 1.mp4
65 MB
18. Time Series Forecasting/20. Checking for Stationarity Part 1.srt
3.3 kB
18. Time Series Forecasting/21. Checking for Stationarity using Statistical Methods Part 2.mp4
75 MB
18. Time Series Forecasting/21. Checking for Stationarity using Statistical Methods Part 2.srt
4.3 kB
18. Time Series Forecasting/22. Checking for Stationary Implementation.mp4
38 MB
18. Time Series Forecasting/22. Checking for Stationary Implementation.srt
3.1 kB
18. Time Series Forecasting/23. Converting Non-Stationary Series into Stationary.mp4
48 MB
18. Time Series Forecasting/23. Converting Non-Stationary Series into Stationary.srt
3.6 kB
18. Time Series Forecasting/24. Converting Non-Stationary Series into Stationary Implementation.mp4
48 MB
18. Time Series Forecasting/24. Converting Non-Stationary Series into Stationary Implementation.srt
3.9 kB
18. Time Series Forecasting/25. Auto Correlation and Partial Correlation.mp4
77 MB
18. Time Series Forecasting/25. Auto Correlation and Partial Correlation.srt
4.1 kB
18. Time Series Forecasting/26. Auto Correlation and Partial Correlation Implementation.mp4
38 MB
18. Time Series Forecasting/26. Auto Correlation and Partial Correlation Implementation.srt
3.6 kB
18. Time Series Forecasting/27. The Simple Auto Regressive Model.mp4
63 MB
18. Time Series Forecasting/27. The Simple Auto Regressive Model.srt
3.2 kB
18. Time Series Forecasting/28. The Simple Auto Regressive Model Implementation.mp4
65 MB
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5. Data Cleaning/16. Introduction to reindex, set_index, reset_index, and sort_index Functions.srt
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5. Data Cleaning/17. Introduction to Replace and Droplevel Function.mp4
33 MB
5. Data Cleaning/17. Introduction to Replace and Droplevel Function.srt
2.5 kB
5. Data Cleaning/18. Introduction to Split and Strip Function.mp4
38 MB
5. Data Cleaning/18. Introduction to Split and Strip Function.srt
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5. Data Cleaning/19. Introduction to Stack, and Unstack Functions.mp4
25 MB
5. Data Cleaning/19. Introduction to Stack, and Unstack Functions.srt
2.1 kB
5. Data Cleaning/2. Types of Missing Values.mp4
62 MB
5. Data Cleaning/2. Types of Missing Values.srt
3.3 kB
5. Data Cleaning/20. Introduction to Melt, Explode, and Squeeze Functions.mp4
41 MB
5. Data Cleaning/20. Introduction to Melt, Explode, and Squeeze Functions.srt
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5. Data Cleaning/21. Data Cleaning on Big Mart Dataset.mp4
38 MB
5. Data Cleaning/21. Data Cleaning on Big Mart Dataset.srt
4.0 kB
5. Data Cleaning/22. Data Cleaning on Movie Dataset.mp4
37 MB
5. Data Cleaning/22. Data Cleaning on Movie Dataset.srt
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5. Data Cleaning/23. Data Cleaning on Melbourne Housing Dataset.mp4
42 MB
5. Data Cleaning/23. Data Cleaning on Melbourne Housing Dataset.srt
3.9 kB
5. Data Cleaning/24. Data Cleaning on Naukri Dataset.mp4
106 MB
5. Data Cleaning/24. Data Cleaning on Naukri Dataset.srt
13 kB
5. Data Cleaning/3. When should we delete the Missing values.mp4
80 MB
5. Data Cleaning/3. When should we delete the Missing values.srt
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5. Data Cleaning/4. Imputing the Missing Values using the Business Logic.mp4
74 MB
5. Data Cleaning/4. Imputing the Missing Values using the Business Logic.srt
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5. Data Cleaning/5. Imputing Missing Values using MeanMedianMode.mp4
56 MB
5. Data Cleaning/5. Imputing Missing Values using MeanMedianMode.srt
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5. Data Cleaning/6. Imputing Missing Values in a real-time scenario.mp4
83 MB
5. Data Cleaning/6. Imputing Missing Values in a real-time scenario.srt
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5. Data Cleaning/7. Quiz on Missing Values Imputation.html
147 B
5. Data Cleaning/8. Quiz Solution.mp4
49 MB
5. Data Cleaning/8. Quiz Solution.srt
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5. Data Cleaning/9. How Outliers can be harmful for Machine Learning Models.mp4
69 MB
5. Data Cleaning/9. How Outliers can be harmful for Machine Learning Models.srt
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6. Data Visualizations/1. Univariate Analysis.mp4
57 MB
6. Data Visualizations/1. Univariate Analysis.srt
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6. Data Visualizations/10. Statistical Charts.mp4
38 MB
6. Data Visualizations/10. Statistical Charts.srt
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6. Data Visualizations/11. Polar Charts.mp4
29 MB
6. Data Visualizations/11. Polar Charts.srt
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6. Data Visualizations/12. Subplots.mp4
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6. Data Visualizations/13. 3D Charts.mp4
25 MB
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6. Data Visualizations/14. Waffle Charts.mp4
29 MB
6. Data Visualizations/14. Waffle Charts.srt
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6. Data Visualizations/15. Maps.mp4
31 MB
6. Data Visualizations/15. Maps.srt
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6. Data Visualizations/16. Quiz on Advanced Visualizations.html
147 B
6. Data Visualizations/17. Quiz Solution.mp4
49 MB
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6. Data Visualizations/18. Animation with Bubbleplot.mp4
48 MB
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6. Data Visualizations/19. Animation with Facets.mp4
27 MB
6. Data Visualizations/19. Animation with Facets.srt
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6. Data Visualizations/2. Bivariate Analysis.mp4
45 MB
6. Data Visualizations/2. Bivariate Analysis.srt
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6. Data Visualizations/20. Animation with Scatter Maps.mp4
23 MB
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6. Data Visualizations/21. Animation with Choropleth Maps.mp4
31 MB
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6. Data Visualizations/22. Quiz on Animated Visualizations.html
147 B
6. Data Visualizations/23. Quiz Solution.mp4
35 MB
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6. Data Visualizations/24. Introduction to Ipywidgets.mp4
39 MB
6. Data Visualizations/24. Introduction to Ipywidgets.srt
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6. Data Visualizations/25. Interactive Univariate Analysis.mp4
30 MB
6. Data Visualizations/25. Interactive Univariate Analysis.srt
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6. Data Visualizations/26. Interactive Bivariate Analysis.mp4
34 MB
6. Data Visualizations/26. Interactive Bivariate Analysis.srt
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6. Data Visualizations/27. Interactive Multivariate Analysis.mp4
29 MB
6. Data Visualizations/27. Interactive Multivariate Analysis.srt
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6. Data Visualizations/28. Quiz on Interactive Visualizations.html
147 B
6. Data Visualizations/29. Quiz Solution.mp4
54 MB
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6. Data Visualizations/3. Multivariate Analysis.mp4
71 MB
6. Data Visualizations/3. Multivariate Analysis.srt
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6. Data Visualizations/30. Sunburst Charts.mp4
33 MB
6. Data Visualizations/30. Sunburst Charts.srt
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6. Data Visualizations/31. Parallel Co-ordinate Charts.mp4
23 MB
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6. Data Visualizations/32. Funnel Charts.mp4
39 MB
6. Data Visualizations/32. Funnel Charts.srt
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6. Data Visualizations/33. Gantt Charts.mp4
25 MB
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6. Data Visualizations/34. Ternary Charts.mp4
20 MB
6. Data Visualizations/34. Ternary Charts.srt
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6. Data Visualizations/35. Tree Maps.mp4
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6. Data Visualizations/36. Network Charts.mp4
40 MB
6. Data Visualizations/36. Network Charts.srt
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6. Data Visualizations/37. Quiz on Miscellaneous Charts.html
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6. Data Visualizations/38. Quiz Solution.mp4
38 MB
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6. Data Visualizations/4. Quiz on Basics of Visualization.html
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6. Data Visualizations/5. Quiz Solution.mp4
47 MB
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6. Data Visualizations/6. Scatter Plots.mp4
45 MB
6. Data Visualizations/6. Scatter Plots.srt
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6. Data Visualizations/7. Charts with Colorscale.mp4
32 MB
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6. Data Visualizations/8. Bar, Line, and Area Charts.mp4
48 MB
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6. Data Visualizations/9. Facet Grids.mp4
38 MB
6. Data Visualizations/9. Facet Grids.srt
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7. Feature Engineering/1. Introduction to Feature Engineering.mp4
60 MB
7. Feature Engineering/1. Introduction to Feature Engineering.srt
3.5 kB
7. Feature Engineering/10. Finding the Words, Characters, and Punctuation Count.mp4
36 MB
7. Feature Engineering/10. Finding the Words, Characters, and Punctuation Count.srt
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7. Feature Engineering/11. Counting Nouns and Verbs in the Text.mp4
31 MB
7. Feature Engineering/11. Counting Nouns and Verbs in the Text.srt
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7. Feature Engineering/12. Counting Adjectives, Adverb, and Pronouns.mp4
24 MB
7. Feature Engineering/12. Counting Adjectives, Adverb, and Pronouns.srt
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7. Feature Engineering/13. Introduction to Assign and Update Functions.mp4
36 MB
7. Feature Engineering/13. Introduction to Assign and Update Functions.srt
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7. Feature Engineering/14. Introduction to at_time and between_time Functions.mp4
30 MB
7. Feature Engineering/14. Introduction to at_time and between_time Functions.srt
3.0 kB
7. Feature Engineering/15. Introduction to nlargest and nsmallest Functions.mp4
35 MB
7. Feature Engineering/15. Introduction to nlargest and nsmallest Functions.srt
3.6 kB
7. Feature Engineering/16. Introduction to Expanding Function.mp4
28 MB
7. Feature Engineering/16. Introduction to Expanding Function.srt
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7. Feature Engineering/17. Introduction to Cumulative Functions.mp4
31 MB
7. Feature Engineering/17. Introduction to Cumulative Functions.srt
3.2 kB
7. Feature Engineering/18. Quiz on Feature Engineering Functions.html
147 B
7. Feature Engineering/19. Quiz Solution.mp4
51 MB
7. Feature Engineering/19. Quiz Solution.srt
3.9 kB
7. Feature Engineering/2. Removing Unnecessary Columns.mp4
57 MB
7. Feature Engineering/2. Removing Unnecessary Columns.srt
4.3 kB
7. Feature Engineering/20. Feature Engineering on Employee Data.mp4
57 MB
7. Feature Engineering/20. Feature Engineering on Employee Data.srt
6.0 kB
7. Feature Engineering/21. Feature Engineering on FIFA Data.mp4
45 MB
7. Feature Engineering/21. Feature Engineering on FIFA Data.srt
4.4 kB
7. Feature Engineering/22. Feature Engineering on Hotel Reviews.mp4
35 MB
7. Feature Engineering/22. Feature Engineering on Hotel Reviews.srt
3.2 kB
7. Feature Engineering/23. Feature Engineering on Marketing Data.mp4
59 MB
7. Feature Engineering/23. Feature Engineering on Marketing Data.srt
6.3 kB
7. Feature Engineering/24. Feature Engineering on Titanic Data.mp4
50 MB
7. Feature Engineering/24. Feature Engineering on Titanic Data.srt
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7. Feature Engineering/25. Quiz on Feature Engineering on Real World Datasets.html
147 B
7. Feature Engineering/26. Quiz Solution.mp4
65 MB
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7. Feature Engineering/3. Decomposing Time and Date Features.mp4
38 MB
7. Feature Engineering/3. Decomposing Time and Date Features.srt
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7. Feature Engineering/4. Decomposing Categorical Features.mp4
38 MB
7. Feature Engineering/4. Decomposing Categorical Features.srt
2.7 kB
7. Feature Engineering/5. Binning Numerical Features.mp4
59 MB
7. Feature Engineering/5. Binning Numerical Features.srt
4.5 kB
7. Feature Engineering/6. Aggregating Features.mp4
57 MB
7. Feature Engineering/6. Aggregating Features.srt
3.9 kB
7. Feature Engineering/7. Introduction to Feature Engineering on Text Data.mp4
34 MB
7. Feature Engineering/7. Introduction to Feature Engineering on Text Data.srt
1.7 kB
7. Feature Engineering/8. Reading and Summarizing the Text.mp4
30 MB
7. Feature Engineering/8. Reading and Summarizing the Text.srt
3.1 kB
7. Feature Engineering/9. Finding the Length, Polarity and Subjectivity.mp4
73 MB
7. Feature Engineering/9. Finding the Length, Polarity and Subjectivity.srt
5.8 kB
8. Data Processing/1. Types of Encoding Techniques.mp4
61 MB
8. Data Processing/1. Types of Encoding Techniques.srt
4.1 kB
8. Data Processing/10. Log transformation.mp4
28 MB
8. Data Processing/10. Log transformation.srt
2.5 kB
8. Data Processing/11. BoxCox transformation.mp4
32 MB
8. Data Processing/11. BoxCox transformation.srt
2.4 kB
8. Data Processing/12. Quiz on Data Transformation.html
147 B
8. Data Processing/13. Train, Test and Validation Split.mp4
44 MB
8. Data Processing/13. Train, Test and Validation Split.srt
3.6 kB
8. Data Processing/14. Standardization and Normalization.mp4
40 MB
8. Data Processing/14. Standardization and Normalization.srt
2.4 kB
8. Data Processing/15. Quiz on Data Splitting and Feature Scaling.html
147 B
8. Data Processing/2. Label Encoding.mp4
34 MB
8. Data Processing/2. Label Encoding.srt
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8. Data Processing/3. Feature Mapping for Ordinal Variables.mp4
29 MB
8. Data Processing/3. Feature Mapping for Ordinal Variables.srt
2.2 kB
8. Data Processing/4. OneHot Encoding.mp4
35 MB
8. Data Processing/4. OneHot Encoding.srt
3.0 kB
8. Data Processing/5. Binary and BaseN Encoding.mp4
33 MB
8. Data Processing/5. Binary and BaseN Encoding.srt
3.4 kB
8. Data Processing/6. Mean and Frequency Encoding.mp4
23 MB
8. Data Processing/6. Mean and Frequency Encoding.srt
2.2 kB
8. Data Processing/7. Quiz on Dealing with Categorical data.html
147 B
8. Data Processing/8. Introduction to Skewness and Normal Distribution.mp4
38 MB
8. Data Processing/8. Introduction to Skewness and Normal Distribution.srt
2.8 kB
8. Data Processing/9. Square and Cube Root Transformation.mp4
39 MB
8. Data Processing/9. Square and Cube Root Transformation.srt
3.3 kB
9. Linear Regression/1. Introduction to Linear Regression.mp4
81 MB
9. Linear Regression/1. Introduction to Linear Regression.srt
4.7 kB
9. Linear Regression/10. Industry relevance of linear regression.mp4
50 MB
9. Linear Regression/10. Industry relevance of linear regression.srt
2.7 kB
9. Linear Regression/11. Quiz on Modelling with Linear Regression.html
147 B
9. Linear Regression/2. Implementing Linear Regression using Sklearn.mp4
74 MB
9. Linear Regression/2. Implementing Linear Regression using Sklearn.srt
11 kB
9. Linear Regression/3. Feature Selection using RFECV.mp4
86 MB
9. Linear Regression/3. Feature Selection using RFECV.srt
6.3 kB
9. Linear Regression/4. Data Transformation with Linear Regression.mp4
58 MB
9. Linear Regression/4. Data Transformation with Linear Regression.srt
6.8 kB
9. Linear Regression/5. Applying Cross Validation.mp4
106 MB
9. Linear Regression/5. Applying Cross Validation.srt
5.2 kB
9. Linear Regression/6. Analyzing the performance of Regression models.mp4
109 MB
9. Linear Regression/6. Analyzing the performance of Regression models.srt
5.0 kB
9. Linear Regression/7. R2 score and adjuted R2 score intuition.mp4
107 MB
9. Linear Regression/7. R2 score and adjuted R2 score intuition.srt
5.9 kB
9. Linear Regression/8. MAE, RMSE, R2 and Adjusted R2 in code.mp4
49 MB
9. Linear Regression/8. MAE, RMSE, R2 and Adjusted R2 in code.srt
5.5 kB
9. Linear Regression/9. Applying real time prediction on our model.mp4
108 MB
9. Linear Regression/9. Applying real time prediction on our model.srt
9.6 kB