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| 1.1 11-Logistic-Regression-Models.zip |
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| 1.1 12-K-Nearest-Neighbors.zip |
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| 1.1 13-Support-Vector-Machines.zip |
1.51Мб |
| 1.1 14-Decision-Trees.zip |
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| 1.1 data_banknote_authentication.csv |
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| 1.2 15-Random-Forests.zip |
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| 1. A note from Jose on Feature Engineering and Data Preparation.html |
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| 1. Capstone Project Overview.mp4 |
93.20Мб |
| 1. Capstone Project Overview.srt |
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| 1. EARLY BIRD INFO.html |
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| 1. Early Bird Note on Downloading .zip for Logistic Regression Notes.html |
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| 1. Introduction to KNN Section.mp4 |
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| 1. Introduction to KNN Section.srt |
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| 1. Introduction to Linear Regression Section.mp4 |
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| 1. Introduction to Linear Regression Section.srt |
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| 1. Introduction to Machine Learning Overview Section.mp4 |
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| 1. Introduction to Machine Learning Overview Section.srt |
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| 1. Introduction to Matplotlib.mp4 |
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| 1. Introduction to Matplotlib.srt |
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| 1. Introduction to NumPy.mp4 |
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| 1. Introduction to NumPy.srt |
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| 1. Introduction to Pandas.mp4 |
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| 1. Introduction to Pandas.srt |
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| 1. Introduction to Random Forests Section.mp4 |
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| 1. Introduction to Random Forests Section.srt |
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| 1. Introduction to Seaborn.mp4 |
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| 1. Introduction to Seaborn.srt |
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| 1. Introduction to Support Vector Machines.mp4 |
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| 1. Introduction to Support Vector Machines.srt |
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| 1. Introduction to Tree Based Methods.mp4 |
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| 1. Introduction to Tree Based Methods.srt |
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| 1. Machine Learning Pathway.mp4 |
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| 1. Machine Learning Pathway.srt |
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| 1. OPTIONAL Python Crash Course.html |
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| 1. Section Overview and Introduction.mp4 |
20.53Мб |
| 1. Section Overview and Introduction.srt |
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| 10. Classification Metrics - Precison, Recall, F1-Score.mp4 |
33.06Мб |
| 10. Classification Metrics - Precison, Recall, F1-Score.srt |
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| 10. Coding Regression with Random Forest Regressor - Part Three - Polynomials.mp4 |
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| 10. Coding Regression with Random Forest Regressor - Part Three - Polynomials.srt |
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| 10. Linear Regression - Residual Plots.mp4 |
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| 10. Linear Regression - Residual Plots.srt |
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| 10. Matplotlib Exercise Questions Overview.mp4 |
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| 10. Matplotlib Exercise Questions Overview.srt |
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| 10. Pandas - Useful Methods - Apply on Single Column.mp4 |
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| 10. Pandas - Useful Methods - Apply on Single Column.srt |
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| 10. Seaborn - Comparison Plots - Coding with Seaborn.mp4 |
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| 10. Seaborn - Comparison Plots - Coding with Seaborn.srt |
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| 10. Support Vector Machine Project Solutions.mp4 |
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| 10. Support Vector Machine Project Solutions.srt |
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| 11. Classification Metrics - ROC Curves.mp4 |
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| 11. Classification Metrics - ROC Curves.srt |
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| 11. Coding Regression with Random Forest Regressor - Part Four - Advanced Models.mp4 |
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| 11. Coding Regression with Random Forest Regressor - Part Four - Advanced Models.srt |
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| 11. Linear Regression - Model Deployment and Coefficient Interpretation.mp4 |
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| 11. Linear Regression - Model Deployment and Coefficient Interpretation.srt |
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| 11. Matplotlib Exercise Questions - Solutions.mp4 |
123.11Мб |
| 11. Matplotlib Exercise Questions - Solutions.srt |
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| 11. Pandas - Useful Methods - Apply on Multiple Columns.mp4 |
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| 11. Pandas - Useful Methods - Apply on Multiple Columns.srt |
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| 11. Seaborn Grid Plots.mp4 |
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| 12. Logistic Regression with Scikit-Learn - Part Three - Performance Evaluation.mp4 |
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| 12. Logistic Regression with Scikit-Learn - Part Three - Performance Evaluation.srt |
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| 12. Pandas - Useful Methods - Statistical Information and Sorting.mp4 |
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| 12. Pandas - Useful Methods - Statistical Information and Sorting.srt |
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| 12. Polynomial Regression - Theory and Motivation.mp4 |
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| 12. Polynomial Regression - Theory and Motivation.srt |
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| 12. Seaborn - Matrix Plots.mp4 |
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| 13. Missing Data - Overview.mp4 |
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| 13. Multi-Class Classification with Logistic Regression - Part One - Data and EDA.mp4 |
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| 13. Multi-Class Classification with Logistic Regression - Part One - Data and EDA.srt |
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| 13. Polynomial Regression - Creating Polynomial Features.mp4 |
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| 13. Polynomial Regression - Creating Polynomial Features.srt |
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| 13. Seaborn Plot Exercises Overview.mp4 |
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| 14. Missing Data - Pandas Operations.mp4 |
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| 14. Missing Data - Pandas Operations.srt |
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| 14. Multi-Class Classification with Logistic Regression - Part Two - Model.mp4 |
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| 14. Multi-Class Classification with Logistic Regression - Part Two - Model.srt |
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| 14. Polynomial Regression - Training and Evaluation.mp4 |
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| 14. Polynomial Regression - Training and Evaluation.srt |
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| 14. Seaborn Plot Exercises Solutions.mp4 |
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| 14. Seaborn Plot Exercises Solutions.srt |
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| 15. Bias Variance Trade-Off.mp4 |
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| 15. Bias Variance Trade-Off.srt |
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| 15. GroupBy Operations - Part One.mp4 |
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| 15. GroupBy Operations - Part One.srt |
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| 15. Logistic Regression Exercise Project Overview.mp4 |
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| 15. Logistic Regression Exercise Project Overview.srt |
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| 16. GroupBy Operations - Part Two - MultiIndex.mp4 |
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| 16. GroupBy Operations - Part Two - MultiIndex.srt |
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| 16. Logistic Regression Project Exercise - Solutions.mp4 |
168.39Мб |
| 16. Logistic Regression Project Exercise - Solutions.srt |
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| 16. Polynomial Regression - Choosing Degree of Polynomial.mp4 |
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| 16. Polynomial Regression - Choosing Degree of Polynomial.srt |
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| 17. Combining DataFrames - Concatenation.mp4 |
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| 17. Combining DataFrames - Concatenation.srt |
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| 17. Polynomial Regression - Model Deployment.mp4 |
28.94Мб |
| 17. Polynomial Regression - Model Deployment.srt |
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| 18. Combining DataFrames - Inner Merge.mp4 |
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| 18. Combining DataFrames - Inner Merge.srt |
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| 18. Regularization Overview.mp4 |
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| 18. Regularization Overview.srt |
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| 19. Combining DataFrames - Left and Right Merge.mp4 |
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| 19. Combining DataFrames - Left and Right Merge.srt |
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| 19. Feature Scaling.mp4 |
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| 19. Feature Scaling.srt |
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| 2.1 UNZIP_ME_FOR_NOTEBOOKS_V4.zip |
35.69Мб |
| 2. Capstone Project Solutions - Part One.mp4 |
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| 2. Capstone Project Solutions - Part One.srt |
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| 2. COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP!.mp4 |
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| 2. COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP!.srt |
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| 2. Cross Validation - Test Train Split.mp4 |
60.46Мб |
| 2. Cross Validation - Test Train Split.srt |
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| 2. Decision Tree - History.mp4 |
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| 2. Decision Tree - History.srt |
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| 2. History of Support Vector Machines.mp4 |
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| 2. History of Support Vector Machines.srt |
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| 2. Introduction to Feature Engineering and Data Preparation.mp4 |
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| 2. Introduction to Feature Engineering and Data Preparation.srt |
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| 2. Introduction to Logistic Regression Section.mp4 |
31.68Мб |
| 2. Introduction to Logistic Regression Section.srt |
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| 2. KNN Classification - Theory and Intuition.mp4 |
50.19Мб |
| 2. KNN Classification - Theory and Intuition.srt |
16.92Кб |
| 2. Linear Regression - Algorithm History.mp4 |
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| 2. Linear Regression - Algorithm History.srt |
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| 2. Matplotlib Basics.mp4 |
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| 2. Matplotlib Basics.srt |
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| 2. NumPy Arrays.mp4 |
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| 2. NumPy Arrays.srt |
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| 2. Python Crash Course - Part One.mp4 |
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| 2. Python Crash Course - Part One.srt |
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| 2. Random Forests - History and Motivation.mp4 |
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| 2. Random Forests - History and Motivation.srt |
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| 2. Scatterplots with Seaborn.mp4 |
128.61Мб |
| 2. Scatterplots with Seaborn.srt |
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| 2. Series - Part One.mp4 |
38.47Мб |
| 2. Series - Part One.srt |
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| 2. Why Machine Learning.mp4 |
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| 2. Why Machine Learning.srt |
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| 20. Combining DataFrames - Outer Merge.mp4 |
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| 20. Combining DataFrames - Outer Merge.srt |
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| 20. Introduction to Cross Validation.mp4 |
62.58Мб |
| 20. Introduction to Cross Validation.srt |
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| 21. Pandas - Text Methods for String Data.mp4 |
75.69Мб |
| 21. Pandas - Text Methods for String Data.srt |
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| 21. Regularization Data Setup.mp4 |
34.44Мб |
| 21. Regularization Data Setup.srt |
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| 22. L2 Regularization - Ridge Regression Theory.mp4 |
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| 22. L2 Regularization - Ridge Regression Theory.srt |
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| 22. Pandas - Time Methods for Date and Time Data.mp4 |
101.92Мб |
| 22. Pandas - Time Methods for Date and Time Data.srt |
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| 23. L2 Regularization - Ridge Regression - Python Implementation.mp4 |
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| 23. L2 Regularization - Ridge Regression - Python Implementation.srt |
26.45Кб |
| 23. Pandas Input and Output - CSV Files.mp4 |
49.87Мб |
| 23. Pandas Input and Output - CSV Files.srt |
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| 24. L1 Regularization - Lasso Regression - Background and Implementation.mp4 |
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| 24. L1 Regularization - Lasso Regression - Background and Implementation.srt |
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| 24. Pandas Input and Output - HTML Tables.mp4 |
106.65Мб |
| 24. Pandas Input and Output - HTML Tables.srt |
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| 25. L1 and L2 Regularization - Elastic Net.mp4 |
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| 25. L1 and L2 Regularization - Elastic Net.srt |
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| 25. Pandas Input and Output - Excel Files.mp4 |
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| 25. Pandas Input and Output - Excel Files.srt |
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| 26. Linear Regression Project - Data Overview.mp4 |
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| 26. Linear Regression Project - Data Overview.srt |
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| 26. Pandas Input and Output - SQL Databases.mp4 |
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| 26. Pandas Input and Output - SQL Databases.srt |
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| 27. Pandas Pivot Tables.mp4 |
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| 27. Pandas Pivot Tables.srt |
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| 28. Pandas Project Exercise Overview.mp4 |
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| 28. Pandas Project Exercise Overview.srt |
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| 29. Pandas Project Exercise Solutions.mp4 |
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| 29. Pandas Project Exercise Solutions.srt |
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| 3.1 UNZIP_ME_FOR_NOTEBOOKS_V4.zip |
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| 3. Anaconda Python and Jupyter Install and Setup.mp4 |
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| 3. Anaconda Python and Jupyter Install and Setup.srt |
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| 3. Capstone Project Solutions - Part Two.mp4 |
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| 3. Check-in Labeled Index in Pandas Series.html |
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| 3. Coding Exercise Check-in Creating NumPy Arrays.html |
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| 3. Cross Validation - Test Validation Train Split.mp4 |
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| 3. Cross Validation - Test Validation Train Split.srt |
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| 3. Dealing with Outliers.mp4 |
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| 3. Decision Tree - Terminology.mp4 |
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| 3. Distribution Plots - Part One - Understanding Plot Types.mp4 |
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| 3. KNN Coding with Python - Part One.mp4 |
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| 3. KNN Coding with Python - Part One.srt |
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| 3. Linear Regression - Understanding Ordinary Least Squares.mp4 |
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| 3. Linear Regression - Understanding Ordinary Least Squares.srt |
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| 3. Logistic Regression - Theory and Intuition - Part One The Logistic Function.mp4 |
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| 3. Logistic Regression - Theory and Intuition - Part One The Logistic Function.srt |
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| 3. Matplotlib - Understanding the Figure Object.mp4 |
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| 3. Python Crash Course - Part Two.mp4 |
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| 3. Random Forests - Key Hyperparameters.mp4 |
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| 3. SVM - Theory and Intuition - Hyperplanes and Margins.mp4 |
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| 3. Types of Machine Learning Algorithms.mp4 |
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| 4. Capstone Project Solutions - Part Three.mp4 |
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| 4. Cross Validation - cross_val_score.mp4 |
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| 4. Dealing with Missing Data Part One - Evaluation of Missing Data.mp4 |
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| 4. Dealing with Missing Data Part One - Evaluation of Missing Data.srt |
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| 4. Decision Tree - Understanding Gini Impurity.mp4 |
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| 4. Distribution Plots - Part Two - Coding with Seaborn.mp4 |
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| 4. KNN Coding with Python - Part Two - Choosing K.mp4 |
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| 4. Linear Regression - Cost Functions.mp4 |
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| 4. Logistic Regression - Theory and Intuition - Part Two Linear to Logistic.mp4 |
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| 4. Matplotlib - Implementing Figures and Axes.mp4 |
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| 4. Note on Environment Setup - Please read me!.html |
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| 4. Python Crash Course - Part Three.mp4 |
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| 4. Random Forests - Number of Estimators and Features in Subsets.mp4 |
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| 4. Series - Part Two.mp4 |
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| 4. Supervised Machine Learning Process.mp4 |
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| 5.1 Backup Google Link for requirements.txt file.html |
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| 5.2 requirements.txt |
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| 5. Categorical Plots - Statistics within Categories - Understanding Plot Types.mp4 |
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| 5. Coding Exercise Check-in Selecting Data from Numpy Array.html |
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| 5. Companion Book - Introduction to Statistical Learning.mp4 |
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| 5. Constructing Decision Trees with Gini Impurity - Part One.mp4 |
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| 5. Constructing Decision Trees with Gini Impurity - Part One.srt |
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| 5. Cross Validation - cross_validate.mp4 |
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| 5. DataFrames - Part One - Creating a DataFrame.mp4 |
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| 5. Dealing with Missing Data Part Two - Filling or Dropping data based on Rows.mp4 |
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| 5. Environment Setup.mp4 |
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| 5. KNN Classification Project Exercise Overview.mp4 |
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| 5. Linear Regression - Gradient Descent.mp4 |
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| 5. Logistic Regression - Theory and Intuition - Linear to Logistic Math.mp4 |
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| 5. Logistic Regression - Theory and Intuition - Linear to Logistic Math.srt |
24.81Кб |
| 5. Matplotlib - Figure Parameters.mp4 |
23.75Мб |
| 5. Matplotlib - Figure Parameters.srt |
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| 5. Python Crash Course - Exercise Questions.mp4 |
5.01Мб |
| 5. Python Crash Course - Exercise Questions.srt |
2.53Кб |
| 5. Random Forests - Bootstrapping and Out-of-Bag Error.mp4 |
63.32Мб |
| 5. Random Forests - Bootstrapping and Out-of-Bag Error.srt |
17.97Кб |
| 5. SVM - Theory and Intuition - Kernel Trick and Mathematics.mp4 |
93.86Мб |
| 5. SVM - Theory and Intuition - Kernel Trick and Mathematics.srt |
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| 50 |
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| 6 |
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| 6. Categorical Plots - Statistics within Categories - Coding with Seaborn.mp4 |
54.99Мб |
| 6. Categorical Plots - Statistics within Categories - Coding with Seaborn.srt |
14.61Кб |
| 6. Coding Classification with Random Forest Classifier - Part One.mp4 |
68.49Мб |
| 6. Coding Classification with Random Forest Classifier - Part One.srt |
18.08Кб |
| 6. Constructing Decision Trees with Gini Impurity - Part Two.mp4 |
52.15Мб |
| 6. Constructing Decision Trees with Gini Impurity - Part Two.srt |
16.42Кб |
| 6. DataFrames - Part Two - Basic Properties.mp4 |
53.90Мб |
| 6. DataFrames - Part Two - Basic Properties.srt |
13.28Кб |
| 6. Dealing with Missing Data Part 3 - Fixing data based on Columns.mp4 |
122.78Мб |
| 6. Dealing with Missing Data Part 3 - Fixing data based on Columns.srt |
36.75Кб |
| 6. Grid Search.mp4 |
78.11Мб |
| 6. Grid Search.srt |
19.26Кб |
| 6. KNN Classification Project Exercise Solutions.mp4 |
109.73Мб |
| 6. KNN Classification Project Exercise Solutions.srt |
21.40Кб |
| 6. Logistic Regression - Theory and Intuition - Best fit with Maximum Likelihood.mp4 |
76.83Мб |
| 6. Logistic Regression - Theory and Intuition - Best fit with Maximum Likelihood.srt |
22.96Кб |
| 6. Matplotlib - Subplots Functionality.mp4 |
96.18Мб |
| 6. Matplotlib - Subplots Functionality.srt |
28.63Кб |
| 6. NumPy Operations.mp4 |
48.59Мб |
| 6. NumPy Operations.srt |
12.05Кб |
| 6. Python coding Simple Linear Regression.mp4 |
91.92Мб |
| 6. Python coding Simple Linear Regression.srt |
28.14Кб |
| 6. Python Crash Course - Exercise Solutions.mp4 |
25.10Мб |
| 6. Python Crash Course - Exercise Solutions.srt |
13.43Кб |
| 6. SVM with Scikit-Learn and Python - Classification Part One.mp4 |
62.71Мб |
| 6. SVM with Scikit-Learn and Python - Classification Part One.srt |
16.38Кб |
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| 7. Categorical Plots - Distributions within Categories - Understanding Plot Types.mp4 |
61.09Мб |
| 7. Categorical Plots - Distributions within Categories - Understanding Plot Types.srt |
20.10Кб |
| 7. Check-In Operations on NumPy Array.html |
163б |
| 7. Coding Classification with Random Forest Classifier - Part Two.mp4 |
139.04Мб |
| 7. Coding Classification with Random Forest Classifier - Part Two.srt |
32.15Кб |
| 7. Coding Decision Trees - Part One - The Data.mp4 |
115.13Мб |
| 7. Coding Decision Trees - Part One - The Data.srt |
29.27Кб |
| 7. DataFrames - Part Three - Working with Columns.mp4 |
89.30Мб |
| 7. DataFrames - Part Three - Working with Columns.srt |
20.61Кб |
| 7. Dealing with Categorical Data - Encoding Options.mp4 |
78.74Мб |
| 7. Dealing with Categorical Data - Encoding Options.srt |
20.10Кб |
| 7. Linear Regression Project Overview.mp4 |
27.48Мб |
| 7. Linear Regression Project Overview.srt |
5.82Кб |
| 7. Logistic Regression with Scikit-Learn - Part One - EDA.mp4 |
73.22Мб |
| 7. Logistic Regression with Scikit-Learn - Part One - EDA.srt |
21.90Кб |
| 7. Matplotlib Styling - Legends.mp4 |
34.10Мб |
| 7. Matplotlib Styling - Legends.srt |
10.35Кб |
| 7. Overview of Scikit-Learn and Python.mp4 |
45.61Мб |
| 7. Overview of Scikit-Learn and Python.srt |
12.34Кб |
| 7. SVM with Scikit-Learn and Python - Classification Part Two.mp4 |
96.60Мб |
| 7. SVM with Scikit-Learn and Python - Classification Part Two.srt |
23.94Кб |
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| 8. Categorical Plots - Distributions within Categories - Coding with Seaborn.mp4 |
111.24Мб |
| 8. Categorical Plots - Distributions within Categories - Coding with Seaborn.srt |
28.26Кб |
| 8. Coding Decision Trees - Part Two -Creating the Model.mp4 |
136.35Мб |
| 8. Coding Decision Trees - Part Two -Creating the Model.srt |
32.69Кб |
| 8. Coding Regression with Random Forest Regressor - Part One - Data.mp4 |
27.61Мб |
| 8. Coding Regression with Random Forest Regressor - Part One - Data.srt |
6.86Кб |
| 8. DataFrames - Part Four - Working with Rows.mp4 |
96.72Мб |
| 8. DataFrames - Part Four - Working with Rows.srt |
21.08Кб |
| 8. Linear Regression Project - Solutions.mp4 |
95.84Мб |
| 8. Linear Regression Project - Solutions.srt |
18.29Кб |
| 8. Linear Regression - Scikit-Learn Train Test Split.mp4 |
82.93Мб |
| 8. Linear Regression - Scikit-Learn Train Test Split.srt |
23.78Кб |
| 8. Logistic Regression with Scikit-Learn - Part Two - Model Training.mp4 |
35.26Мб |
| 8. Logistic Regression with Scikit-Learn - Part Two - Model Training.srt |
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| 8. Matplotlib Styling - Colors and Styles.mp4 |
81.19Мб |
| 8. Matplotlib Styling - Colors and Styles.srt |
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| 8. NumPy Exercises.mp4 |
11.52Мб |
| 8. NumPy Exercises.srt |
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| 8. SVM with Scikit-Learn and Python - Regression Tasks.mp4 |
99.27Мб |
| 8. SVM with Scikit-Learn and Python - Regression Tasks.srt |
29.99Кб |
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910.45Кб |
| 9. Advanced Matplotlib Commands (Optional).mp4 |
40.44Мб |
| 9. Advanced Matplotlib Commands (Optional).srt |
6.49Кб |
| 9. Classification Metrics - Confusion Matrix and Accuracy.mp4 |
46.99Мб |
| 9. Classification Metrics - Confusion Matrix and Accuracy.srt |
13.92Кб |
| 9. Coding Regression with Random Forest Regressor - Part Two - Basic Models.mp4 |
89.73Мб |
| 9. Coding Regression with Random Forest Regressor - Part Two - Basic Models.srt |
20.42Кб |
| 9. Linear Regression - Scikit-Learn Performance Evaluation - Regression.mp4 |
73.16Мб |
| 9. Linear Regression - Scikit-Learn Performance Evaluation - Regression.srt |
23.00Кб |
| 9. Numpy Exercises - Solutions.mp4 |
48.57Мб |
| 9. Numpy Exercises - Solutions.srt |
10.87Кб |
| 9. Pandas - Conditional Filtering.mp4 |
90.05Мб |
| 9. Pandas - Conditional Filtering.srt |
27.14Кб |
| 9. Seaborn - Comparison Plots - Understanding the Plot Types.mp4 |
23.35Мб |
| 9. Seaborn - Comparison Plots - Understanding the Plot Types.srt |
8.73Кб |
| 9. Support Vector Machine Project Overview.mp4 |
40.46Мб |
| 9. Support Vector Machine Project Overview.srt |
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| TutsNode.com.txt |
63б |