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| [FreeCourseSite.com].url |
127б |
| 1. Capstone Project Overview.mp4 |
93.20Мб |
| 1. Capstone Project Overview.srt |
20.60Кб |
| 1. EARLY BIRD INFO.html |
550б |
| 1. Introduction to Linear Regression Section.mp4 |
8.87Мб |
| 1. Introduction to Linear Regression Section.srt |
2.68Кб |
| 1. Introduction to Machine Learning Overview Section.mp4 |
29.73Мб |
| 1. Introduction to Machine Learning Overview Section.srt |
8.58Кб |
| 1. Introduction to Matplotlib.mp4 |
21.57Мб |
| 1. Introduction to Matplotlib.srt |
6.72Кб |
| 1. Introduction to NumPy.mp4 |
11.28Мб |
| 1. Introduction to NumPy.srt |
3.01Кб |
| 1. Introduction to Pandas.mp4 |
21.01Мб |
| 1. Introduction to Pandas.srt |
7.24Кб |
| 1. Introduction to Seaborn.mp4 |
20.01Мб |
| 1. Introduction to Seaborn.srt |
6.51Кб |
| 1. Machine Learning Pathway.mp4 |
40.55Мб |
| 1. Machine Learning Pathway.srt |
15.79Кб |
| 1. OPTIONAL Python Crash Course.html |
472б |
| 10. Linear Regression - Residual Plots.mp4 |
59.52Мб |
| 10. Linear Regression - Residual Plots.srt |
20.22Кб |
| 10. Matplotlib Exercise Questions Overview.mp4 |
50.77Мб |
| 10. Matplotlib Exercise Questions Overview.srt |
9.33Кб |
| 10. Pandas - Useful Methods - Apply on Multiple Columns.mp4 |
98.55Мб |
| 10. Pandas - Useful Methods - Apply on Multiple Columns.srt |
25.93Кб |
| 10. Seaborn - Comparison Plots - Coding with Seaborn.mp4 |
70.16Мб |
| 10. Seaborn - Comparison Plots - Coding with Seaborn.srt |
15.70Кб |
| 11. Linear Regression - Model Deployment and Coefficient Interpretation.mp4 |
88.19Мб |
| 11. Linear Regression - Model Deployment and Coefficient Interpretation.srt |
25.62Кб |
| 11. Matplotlib Exercise Questions - Solutions.mp4 |
123.11Мб |
| 11. Matplotlib Exercise Questions - Solutions.srt |
24.53Кб |
| 11. Pandas - Useful Methods - Statistical Information and Sorting.mp4 |
85.65Мб |
| 11. Pandas - Useful Methods - Statistical Information and Sorting.srt |
23.40Кб |
| 11. Seaborn Grid Plots.mp4 |
91.62Мб |
| 11. Seaborn Grid Plots.srt |
20.50Кб |
| 12. Missing Data - Overview.mp4 |
53.18Мб |
| 12. Missing Data - Overview.srt |
18.36Кб |
| 12. Polynomial Regression - Theory and Motivation.mp4 |
44.47Мб |
| 12. Polynomial Regression - Theory and Motivation.srt |
11.20Кб |
| 12. Seaborn - Matrix Plots.mp4 |
71.25Мб |
| 12. Seaborn - Matrix Plots.srt |
21.09Кб |
| 13. Missing Data - Pandas Operations.mp4 |
97.86Мб |
| 13. Missing Data - Pandas Operations.srt |
27.41Кб |
| 13. Polynomial Regression - Creating Polynomial Features.mp4 |
52.62Мб |
| 13. Polynomial Regression - Creating Polynomial Features.srt |
16.39Кб |
| 13. Seaborn Plot Exercises Overview.mp4 |
49.91Мб |
| 13. Seaborn Plot Exercises Overview.srt |
11.26Кб |
| 14. GroupBy Operations - Part One.mp4 |
93.11Мб |
| 14. GroupBy Operations - Part One.srt |
21.41Кб |
| 14. Polynomial Regression - Training and Evaluation.mp4 |
48.86Мб |
| 14. Polynomial Regression - Training and Evaluation.srt |
14.17Кб |
| 14. Seaborn Plot Exercises Solutions.mp4 |
110.60Мб |
| 14. Seaborn Plot Exercises Solutions.srt |
22.40Кб |
| 15. Bias Variance Trade-Off.mp4 |
43.04Мб |
| 15. Bias Variance Trade-Off.srt |
15.94Кб |
| 15. GroupBy Operations - Part Two - MultiIndex.mp4 |
105.86Мб |
| 15. GroupBy Operations - Part Two - MultiIndex.srt |
20.86Кб |
| 16. Combining DataFrames - Concatenation.mp4 |
50.51Мб |
| 16. Combining DataFrames - Concatenation.srt |
15.02Кб |
| 16. Polynomial Regression - Choosing Degree of Polynomial.mp4 |
72.93Мб |
| 16. Polynomial Regression - Choosing Degree of Polynomial.srt |
19.88Кб |
| 17. Combining DataFrames - Inner Merge.mp4 |
53.61Мб |
| 17. Combining DataFrames - Inner Merge.srt |
18.52Кб |
| 17. Polynomial Regression - Model Deployment.mp4 |
28.94Мб |
| 17. Polynomial Regression - Model Deployment.srt |
8.38Кб |
| 18. Combining DataFrames - Left and Right Merge.mp4 |
27.90Мб |
| 18. Combining DataFrames - Left and Right Merge.srt |
9.10Кб |
| 18. Regularization Overview.mp4 |
33.34Мб |
| 18. Regularization Overview.srt |
10.33Кб |
| 19. Combining DataFrames - Outer Merge.mp4 |
39.89Мб |
| 19. Combining DataFrames - Outer Merge.srt |
14.57Кб |
| 19. Feature Scaling.mp4 |
53.97Мб |
| 19. Feature Scaling.srt |
14.83Кб |
| 2.1 UNZIP_ME_FOR_NOTEBOOKS.zip |
27.09Мб |
| 2. Capstone Project Solutions - Part One.mp4 |
116.95Мб |
| 2. Capstone Project Solutions - Part One.srt |
26.84Кб |
| 2. COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP!.mp4 |
24.55Мб |
| 2. COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP!.srt |
7.17Кб |
| 2. Linear Regression - Algorithm History.mp4 |
54.71Мб |
| 2. Linear Regression - Algorithm History.srt |
13.09Кб |
| 2. Matplotlib Basics.mp4 |
53.61Мб |
| 2. Matplotlib Basics.srt |
19.64Кб |
| 2. NumPy Arrays.mp4 |
109.63Мб |
| 2. NumPy Arrays.srt |
31.91Кб |
| 2. Python Crash Course - Part One.mp4 |
29.52Мб |
| 2. Python Crash Course - Part One.srt |
24.63Кб |
| 2. Scatterplots with Seaborn.mp4 |
128.61Мб |
| 2. Scatterplots with Seaborn.srt |
29.72Кб |
| 2. Series - Part One.mp4 |
38.48Мб |
| 2. Series - Part One.srt |
13.39Кб |
| 2. Why Machine Learning.mp4 |
44.77Мб |
| 2. Why Machine Learning.srt |
14.66Кб |
| 20. Introduction to Cross Validation.mp4 |
62.59Мб |
| 20. Introduction to Cross Validation.srt |
19.81Кб |
| 20. Pandas - Text Methods for String Data.mp4 |
75.69Мб |
| 20. Pandas - Text Methods for String Data.srt |
23.95Кб |
| 21. Pandas - Time Methods for Date and Time Data.mp4 |
101.92Мб |
| 21. Pandas - Time Methods for Date and Time Data.srt |
31.72Кб |
| 21. Regularization Data Setup.mp4 |
34.45Мб |
| 21. Regularization Data Setup.srt |
12.42Кб |
| 22. L2 Regularization - Ridge Regression Theory.mp4 |
61.09Мб |
| 22. L2 Regularization - Ridge Regression Theory.srt |
20.72Кб |
| 22. Pandas Input and Output - CSV Files.mp4 |
49.87Мб |
| 22. Pandas Input and Output - CSV Files.srt |
16.59Кб |
| 23. L2 Regularization - Ridge Regression - Python Implementation.mp4 |
96.42Мб |
| 23. L2 Regularization - Ridge Regression - Python Implementation.srt |
26.45Кб |
| 23. Pandas Input and Output - HTML Tables.mp4 |
106.65Мб |
| 23. Pandas Input and Output - HTML Tables.srt |
22.36Кб |
| 24. L1 Regularization - Lasso Regression - Background and Implementation.mp4 |
100.00Мб |
| 24. L1 Regularization - Lasso Regression - Background and Implementation.srt |
22.44Кб |
| 24. Pandas Input and Output - Excel Files.mp4 |
34.58Мб |
| 24. Pandas Input and Output - Excel Files.srt |
10.88Кб |
| 25. L1 and L2 Regularization - Elastic Net.mp4 |
93.41Мб |
| 25. L1 and L2 Regularization - Elastic Net.srt |
25.72Кб |
| 25. Pandas Input and Output - SQL Databases.mp4 |
103.19Мб |
| 25. Pandas Input and Output - SQL Databases.srt |
29.43Кб |
| 26. Linear Regression Project - Data Overview.mp4 |
39.07Мб |
| 26. Linear Regression Project - Data Overview.srt |
7.67Кб |
| 26. Pandas Pivot Tables.mp4 |
128.74Мб |
| 26. Pandas Pivot Tables.srt |
32.18Кб |
| 27. Pandas Project Exercise Overview.mp4 |
41.07Мб |
| 27. Pandas Project Exercise Overview.srt |
9.59Кб |
| 28. Pandas Project Exercise Solutions.mp4 |
181.59Мб |
| 28. Pandas Project Exercise Solutions.srt |
38.76Кб |
| 3.1 UNZIP_ME_FOR_NOTEBOOKS.zip |
27.09Мб |
| 3. Anaconda Python and Jupyter Install and Setup.mp4 |
98.75Мб |
| 3. Anaconda Python and Jupyter Install and Setup.srt |
21.55Кб |
| 3. Capstone Project Solutions - Part Two.mp4 |
111.05Мб |
| 3. Capstone Project Solutions - Part Two.srt |
23.48Кб |
| 3. Coding Exercise Check-in Creating NumPy Arrays.html |
163б |
| 3. Distribution Plots - Part One - Understanding Plot Types.mp4 |
32.05Мб |
| 3. Distribution Plots - Part One - Understanding Plot Types.srt |
15.00Кб |
| 3. Linear Regression - Understanding Ordinary Least Squares.mp4 |
86.26Мб |
| 3. Linear Regression - Understanding Ordinary Least Squares.srt |
22.52Кб |
| 3. Matplotlib - Understanding the Figure Object.mp4 |
25.81Мб |
| 3. Matplotlib - Understanding the Figure Object.srt |
11.55Кб |
| 3. Python Crash Course - Part Two.mp4 |
22.25Мб |
| 3. Python Crash Course - Part Two.srt |
18.03Кб |
| 3. Series - Part Two.mp4 |
45.30Мб |
| 3. Series - Part Two.srt |
15.37Кб |
| 3. Types of Machine Learning Algorithms.mp4 |
38.68Мб |
| 3. Types of Machine Learning Algorithms.srt |
11.63Кб |
| 4.1 Backup Google Link for requirements.txt file.html |
143б |
| 4.2 requirements.txt |
221б |
| 4. Capstone Project Solutions - Part Three.mp4 |
143.96Мб |
| 4. Capstone Project Solutions - Part Three.srt |
30.88Кб |
| 4. DataFrames - Part One - Creating a DataFrame.mp4 |
114.08Мб |
| 4. DataFrames - Part One - Creating a DataFrame.srt |
29.00Кб |
| 4. Distribution Plots - Part Two - Coding with Seaborn.mp4 |
77.74Мб |
| 4. Distribution Plots - Part Two - Coding with Seaborn.srt |
24.79Кб |
| 4. Environment Setup.mp4 |
49.32Мб |
| 4. Environment Setup.srt |
14.49Кб |
| 4. Linear Regression - Cost Functions.mp4 |
36.02Мб |
| 4. Linear Regression - Cost Functions.srt |
11.46Кб |
| 4. Matplotlib - Implementing Figures and Axes.mp4 |
59.09Мб |
| 4. Matplotlib - Implementing Figures and Axes.srt |
20.97Кб |
| 4. NumPy Indexing and Selection.mp4 |
46.35Мб |
| 4. NumPy Indexing and Selection.srt |
16.22Кб |
| 4. Python Crash Course - Part Three.mp4 |
23.17Мб |
| 4. Python Crash Course - Part Three.srt |
16.57Кб |
| 4. Supervised Machine Learning Process.mp4 |
71.41Мб |
| 4. Supervised Machine Learning Process.srt |
19.76Кб |
| 5. Categorical Plots - Statistics within Categories - Understanding Plot Types.mp4 |
21.86Мб |
| 5. Categorical Plots - Statistics within Categories - Understanding Plot Types.srt |
8.80Кб |
| 5. Companion Book - Introduction to Statistical Learning.mp4 |
19.29Мб |
| 5. Companion Book - Introduction to Statistical Learning.srt |
4.66Кб |
| 5. DataFrames - Part Two - Basic Properties.mp4 |
53.91Мб |
| 5. DataFrames - Part Two - Basic Properties.srt |
13.28Кб |
| 5. Linear Regression - Gradient Descent.mp4 |
65.04Мб |
| 5. Linear Regression - Gradient Descent.srt |
16.73Кб |
| 5. Matplotlib - Figure Parameters.mp4 |
23.75Мб |
| 5. Matplotlib - Figure Parameters.srt |
7.65Кб |
| 5. NumPy Operations.mp4 |
48.59Мб |
| 5. NumPy Operations.srt |
12.05Кб |
| 5. Python Crash Course - Exercise Questions.mp4 |
5.00Мб |
| 5. Python Crash Course - Exercise Questions.srt |
2.53Кб |
| 6. Categorical Plots - Statistics within Categories - Coding with Seaborn.mp4 |
55.00Мб |
| 6. Categorical Plots - Statistics within Categories - Coding with Seaborn.srt |
14.61Кб |
| 6. DataFrames - Part Three - Working with Columns.mp4 |
89.30Мб |
| 6. DataFrames - Part Three - Working with Columns.srt |
20.61Кб |
| 6. Matplotlib - Subplots Functionality.mp4 |
96.18Мб |
| 6. Matplotlib - Subplots Functionality.srt |
28.63Кб |
| 6. NumPy Exercises.mp4 |
11.52Мб |
| 6. NumPy Exercises.srt |
2.07Кб |
| 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 |
| 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. DataFrames - Part Four - Working with Rows.mp4 |
96.71Мб |
| 7. DataFrames - Part Four - Working with Rows.srt |
21.08Кб |
| 7. Matplotlib Styling - Legends.mp4 |
34.09Мб |
| 7. Matplotlib Styling - Legends.srt |
10.35Кб |
| 7. Numpy Exercises - Solutions.mp4 |
48.56Мб |
| 7. Numpy Exercises - Solutions.srt |
10.87Кб |
| 7. Overview of Scikit-Learn and Python.mp4 |
45.61Мб |
| 7. Overview of Scikit-Learn and Python.srt |
12.34Кб |
| 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. Linear Regression - Scikit-Learn Train Test Split.mp4 |
82.93Мб |
| 8. Linear Regression - Scikit-Learn Train Test Split.srt |
23.78Кб |
| 8. Matplotlib Styling - Colors and Styles.mp4 |
81.19Мб |
| 8. Matplotlib Styling - Colors and Styles.srt |
21.04Кб |
| 8. Pandas - Conditional Filtering.mp4 |
90.04Мб |
| 8. Pandas - Conditional Filtering.srt |
27.14Кб |
| 9. Advanced Matplotlib Commands (Optional).mp4 |
40.44Мб |
| 9. Advanced Matplotlib Commands (Optional).srt |
6.49Кб |
| 9. Linear Regression - Scikit-Learn Performance Evaluation - Regression.mp4 |
73.64Мб |
| 9. Linear Regression - Scikit-Learn Performance Evaluation - Regression.srt |
22.83Кб |
| 9. Pandas - Useful Methods - Apply on Single Column.mp4 |
73.05Мб |
| 9. Pandas - Useful Methods - Apply on Single Column.srt |
20.23Кб |
| 9. Seaborn - Comparison Plots - Understanding the Plot Types.mp4 |
23.35Мб |
| 9. Seaborn - Comparison Plots - Understanding the Plot Types.srt |
8.73Кб |