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| [FreeCourseSite.com].url |
127B |
| 1. Capstone Project Overview.mp4 |
93.20MB |
| 1. Capstone Project Overview.srt |
20.60KB |
| 1. EARLY BIRD INFO.html |
550B |
| 1. Introduction to Linear Regression Section.mp4 |
8.87MB |
| 1. Introduction to Linear Regression Section.srt |
2.68KB |
| 1. Introduction to Machine Learning Overview Section.mp4 |
29.73MB |
| 1. Introduction to Machine Learning Overview Section.srt |
8.58KB |
| 1. Introduction to Matplotlib.mp4 |
21.57MB |
| 1. Introduction to Matplotlib.srt |
6.72KB |
| 1. Introduction to NumPy.mp4 |
11.28MB |
| 1. Introduction to NumPy.srt |
3.01KB |
| 1. Introduction to Pandas.mp4 |
21.01MB |
| 1. Introduction to Pandas.srt |
7.24KB |
| 1. Introduction to Seaborn.mp4 |
20.01MB |
| 1. Introduction to Seaborn.srt |
6.51KB |
| 1. Machine Learning Pathway.mp4 |
40.55MB |
| 1. Machine Learning Pathway.srt |
15.79KB |
| 1. OPTIONAL Python Crash Course.html |
472B |
| 10. Linear Regression - Residual Plots.mp4 |
59.52MB |
| 10. Linear Regression - Residual Plots.srt |
20.22KB |
| 10. Matplotlib Exercise Questions Overview.mp4 |
50.77MB |
| 10. Matplotlib Exercise Questions Overview.srt |
9.33KB |
| 10. Pandas - Useful Methods - Apply on Multiple Columns.mp4 |
98.55MB |
| 10. Pandas - Useful Methods - Apply on Multiple Columns.srt |
25.93KB |
| 10. Seaborn - Comparison Plots - Coding with Seaborn.mp4 |
70.16MB |
| 10. Seaborn - Comparison Plots - Coding with Seaborn.srt |
15.70KB |
| 11. Linear Regression - Model Deployment and Coefficient Interpretation.mp4 |
88.19MB |
| 11. Linear Regression - Model Deployment and Coefficient Interpretation.srt |
25.62KB |
| 11. Matplotlib Exercise Questions - Solutions.mp4 |
123.11MB |
| 11. Matplotlib Exercise Questions - Solutions.srt |
24.53KB |
| 11. Pandas - Useful Methods - Statistical Information and Sorting.mp4 |
85.65MB |
| 11. Pandas - Useful Methods - Statistical Information and Sorting.srt |
23.40KB |
| 11. Seaborn Grid Plots.mp4 |
91.62MB |
| 11. Seaborn Grid Plots.srt |
20.50KB |
| 12. Missing Data - Overview.mp4 |
53.18MB |
| 12. Missing Data - Overview.srt |
18.36KB |
| 12. Polynomial Regression - Theory and Motivation.mp4 |
44.47MB |
| 12. Polynomial Regression - Theory and Motivation.srt |
11.20KB |
| 12. Seaborn - Matrix Plots.mp4 |
71.25MB |
| 12. Seaborn - Matrix Plots.srt |
21.09KB |
| 13. Missing Data - Pandas Operations.mp4 |
97.86MB |
| 13. Missing Data - Pandas Operations.srt |
27.41KB |
| 13. Polynomial Regression - Creating Polynomial Features.mp4 |
52.62MB |
| 13. Polynomial Regression - Creating Polynomial Features.srt |
16.39KB |
| 13. Seaborn Plot Exercises Overview.mp4 |
49.91MB |
| 13. Seaborn Plot Exercises Overview.srt |
11.26KB |
| 14. GroupBy Operations - Part One.mp4 |
93.11MB |
| 14. GroupBy Operations - Part One.srt |
21.41KB |
| 14. Polynomial Regression - Training and Evaluation.mp4 |
48.86MB |
| 14. Polynomial Regression - Training and Evaluation.srt |
14.17KB |
| 14. Seaborn Plot Exercises Solutions.mp4 |
110.60MB |
| 14. Seaborn Plot Exercises Solutions.srt |
22.40KB |
| 15. Bias Variance Trade-Off.mp4 |
43.04MB |
| 15. Bias Variance Trade-Off.srt |
15.94KB |
| 15. GroupBy Operations - Part Two - MultiIndex.mp4 |
105.86MB |
| 15. GroupBy Operations - Part Two - MultiIndex.srt |
20.86KB |
| 16. Combining DataFrames - Concatenation.mp4 |
50.51MB |
| 16. Combining DataFrames - Concatenation.srt |
15.02KB |
| 16. Polynomial Regression - Choosing Degree of Polynomial.mp4 |
72.93MB |
| 16. Polynomial Regression - Choosing Degree of Polynomial.srt |
19.88KB |
| 17. Combining DataFrames - Inner Merge.mp4 |
53.61MB |
| 17. Combining DataFrames - Inner Merge.srt |
18.52KB |
| 17. Polynomial Regression - Model Deployment.mp4 |
28.94MB |
| 17. Polynomial Regression - Model Deployment.srt |
8.38KB |
| 18. Combining DataFrames - Left and Right Merge.mp4 |
27.90MB |
| 18. Combining DataFrames - Left and Right Merge.srt |
9.10KB |
| 18. Regularization Overview.mp4 |
33.34MB |
| 18. Regularization Overview.srt |
10.33KB |
| 19. Combining DataFrames - Outer Merge.mp4 |
39.89MB |
| 19. Combining DataFrames - Outer Merge.srt |
14.57KB |
| 19. Feature Scaling.mp4 |
53.97MB |
| 19. Feature Scaling.srt |
14.83KB |
| 2.1 UNZIP_ME_FOR_NOTEBOOKS.zip |
27.09MB |
| 2. Capstone Project Solutions - Part One.mp4 |
116.95MB |
| 2. Capstone Project Solutions - Part One.srt |
26.84KB |
| 2. COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP!.mp4 |
24.55MB |
| 2. COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP!.srt |
7.17KB |
| 2. Linear Regression - Algorithm History.mp4 |
54.71MB |
| 2. Linear Regression - Algorithm History.srt |
13.09KB |
| 2. Matplotlib Basics.mp4 |
53.61MB |
| 2. Matplotlib Basics.srt |
19.64KB |
| 2. NumPy Arrays.mp4 |
109.63MB |
| 2. NumPy Arrays.srt |
31.91KB |
| 2. Python Crash Course - Part One.mp4 |
29.52MB |
| 2. Python Crash Course - Part One.srt |
24.63KB |
| 2. Scatterplots with Seaborn.mp4 |
128.61MB |
| 2. Scatterplots with Seaborn.srt |
29.72KB |
| 2. Series - Part One.mp4 |
38.48MB |
| 2. Series - Part One.srt |
13.39KB |
| 2. Why Machine Learning.mp4 |
44.77MB |
| 2. Why Machine Learning.srt |
14.66KB |
| 20. Introduction to Cross Validation.mp4 |
62.59MB |
| 20. Introduction to Cross Validation.srt |
19.81KB |
| 20. Pandas - Text Methods for String Data.mp4 |
75.69MB |
| 20. Pandas - Text Methods for String Data.srt |
23.95KB |
| 21. Pandas - Time Methods for Date and Time Data.mp4 |
101.92MB |
| 21. Pandas - Time Methods for Date and Time Data.srt |
31.72KB |
| 21. Regularization Data Setup.mp4 |
34.45MB |
| 21. Regularization Data Setup.srt |
12.42KB |
| 22. L2 Regularization - Ridge Regression Theory.mp4 |
61.09MB |
| 22. L2 Regularization - Ridge Regression Theory.srt |
20.72KB |
| 22. Pandas Input and Output - CSV Files.mp4 |
49.87MB |
| 22. Pandas Input and Output - CSV Files.srt |
16.59KB |
| 23. L2 Regularization - Ridge Regression - Python Implementation.mp4 |
96.42MB |
| 23. L2 Regularization - Ridge Regression - Python Implementation.srt |
26.45KB |
| 23. Pandas Input and Output - HTML Tables.mp4 |
106.65MB |
| 23. Pandas Input and Output - HTML Tables.srt |
22.36KB |
| 24. L1 Regularization - Lasso Regression - Background and Implementation.mp4 |
100.00MB |
| 24. L1 Regularization - Lasso Regression - Background and Implementation.srt |
22.44KB |
| 24. Pandas Input and Output - Excel Files.mp4 |
34.58MB |
| 24. Pandas Input and Output - Excel Files.srt |
10.88KB |
| 25. L1 and L2 Regularization - Elastic Net.mp4 |
93.41MB |
| 25. L1 and L2 Regularization - Elastic Net.srt |
25.72KB |
| 25. Pandas Input and Output - SQL Databases.mp4 |
103.19MB |
| 25. Pandas Input and Output - SQL Databases.srt |
29.43KB |
| 26. Linear Regression Project - Data Overview.mp4 |
39.07MB |
| 26. Linear Regression Project - Data Overview.srt |
7.67KB |
| 26. Pandas Pivot Tables.mp4 |
128.74MB |
| 26. Pandas Pivot Tables.srt |
32.18KB |
| 27. Pandas Project Exercise Overview.mp4 |
41.07MB |
| 27. Pandas Project Exercise Overview.srt |
9.59KB |
| 28. Pandas Project Exercise Solutions.mp4 |
181.59MB |
| 28. Pandas Project Exercise Solutions.srt |
38.76KB |
| 3.1 UNZIP_ME_FOR_NOTEBOOKS.zip |
27.09MB |
| 3. Anaconda Python and Jupyter Install and Setup.mp4 |
98.75MB |
| 3. Anaconda Python and Jupyter Install and Setup.srt |
21.55KB |
| 3. Capstone Project Solutions - Part Two.mp4 |
111.05MB |
| 3. Capstone Project Solutions - Part Two.srt |
23.48KB |
| 3. Coding Exercise Check-in Creating NumPy Arrays.html |
163B |
| 3. Distribution Plots - Part One - Understanding Plot Types.mp4 |
32.05MB |
| 3. Distribution Plots - Part One - Understanding Plot Types.srt |
15.00KB |
| 3. Linear Regression - Understanding Ordinary Least Squares.mp4 |
86.26MB |
| 3. Linear Regression - Understanding Ordinary Least Squares.srt |
22.52KB |
| 3. Matplotlib - Understanding the Figure Object.mp4 |
25.81MB |
| 3. Matplotlib - Understanding the Figure Object.srt |
11.55KB |
| 3. Python Crash Course - Part Two.mp4 |
22.25MB |
| 3. Python Crash Course - Part Two.srt |
18.03KB |
| 3. Series - Part Two.mp4 |
45.30MB |
| 3. Series - Part Two.srt |
15.37KB |
| 3. Types of Machine Learning Algorithms.mp4 |
38.68MB |
| 3. Types of Machine Learning Algorithms.srt |
11.63KB |
| 4.1 Backup Google Link for requirements.txt file.html |
143B |
| 4.2 requirements.txt |
221B |
| 4. Capstone Project Solutions - Part Three.mp4 |
143.96MB |
| 4. Capstone Project Solutions - Part Three.srt |
30.88KB |
| 4. DataFrames - Part One - Creating a DataFrame.mp4 |
114.08MB |
| 4. DataFrames - Part One - Creating a DataFrame.srt |
29.00KB |
| 4. Distribution Plots - Part Two - Coding with Seaborn.mp4 |
77.74MB |
| 4. Distribution Plots - Part Two - Coding with Seaborn.srt |
24.79KB |
| 4. Environment Setup.mp4 |
49.32MB |
| 4. Environment Setup.srt |
14.49KB |
| 4. Linear Regression - Cost Functions.mp4 |
36.02MB |
| 4. Linear Regression - Cost Functions.srt |
11.46KB |
| 4. Matplotlib - Implementing Figures and Axes.mp4 |
59.09MB |
| 4. Matplotlib - Implementing Figures and Axes.srt |
20.97KB |
| 4. NumPy Indexing and Selection.mp4 |
46.35MB |
| 4. NumPy Indexing and Selection.srt |
16.22KB |
| 4. Python Crash Course - Part Three.mp4 |
23.17MB |
| 4. Python Crash Course - Part Three.srt |
16.57KB |
| 4. Supervised Machine Learning Process.mp4 |
71.41MB |
| 4. Supervised Machine Learning Process.srt |
19.76KB |
| 5. Categorical Plots - Statistics within Categories - Understanding Plot Types.mp4 |
21.86MB |
| 5. Categorical Plots - Statistics within Categories - Understanding Plot Types.srt |
8.80KB |
| 5. Companion Book - Introduction to Statistical Learning.mp4 |
19.29MB |
| 5. Companion Book - Introduction to Statistical Learning.srt |
4.66KB |
| 5. DataFrames - Part Two - Basic Properties.mp4 |
53.91MB |
| 5. DataFrames - Part Two - Basic Properties.srt |
13.28KB |
| 5. Linear Regression - Gradient Descent.mp4 |
65.04MB |
| 5. Linear Regression - Gradient Descent.srt |
16.73KB |
| 5. Matplotlib - Figure Parameters.mp4 |
23.75MB |
| 5. Matplotlib - Figure Parameters.srt |
7.65KB |
| 5. NumPy Operations.mp4 |
48.59MB |
| 5. NumPy Operations.srt |
12.05KB |
| 5. Python Crash Course - Exercise Questions.mp4 |
5.00MB |
| 5. Python Crash Course - Exercise Questions.srt |
2.53KB |
| 6. Categorical Plots - Statistics within Categories - Coding with Seaborn.mp4 |
55.00MB |
| 6. Categorical Plots - Statistics within Categories - Coding with Seaborn.srt |
14.61KB |
| 6. DataFrames - Part Three - Working with Columns.mp4 |
89.30MB |
| 6. DataFrames - Part Three - Working with Columns.srt |
20.61KB |
| 6. Matplotlib - Subplots Functionality.mp4 |
96.18MB |
| 6. Matplotlib - Subplots Functionality.srt |
28.63KB |
| 6. NumPy Exercises.mp4 |
11.52MB |
| 6. NumPy Exercises.srt |
2.07KB |
| 6. Python coding Simple Linear Regression.mp4 |
91.92MB |
| 6. Python coding Simple Linear Regression.srt |
28.14KB |
| 6. Python Crash Course - Exercise Solutions.mp4 |
25.10MB |
| 6. Python Crash Course - Exercise Solutions.srt |
13.43KB |
| 7. Categorical Plots - Distributions within Categories - Understanding Plot Types.mp4 |
61.09MB |
| 7. Categorical Plots - Distributions within Categories - Understanding Plot Types.srt |
20.10KB |
| 7. DataFrames - Part Four - Working with Rows.mp4 |
96.71MB |
| 7. DataFrames - Part Four - Working with Rows.srt |
21.08KB |
| 7. Matplotlib Styling - Legends.mp4 |
34.09MB |
| 7. Matplotlib Styling - Legends.srt |
10.35KB |
| 7. Numpy Exercises - Solutions.mp4 |
48.56MB |
| 7. Numpy Exercises - Solutions.srt |
10.87KB |
| 7. Overview of Scikit-Learn and Python.mp4 |
45.61MB |
| 7. Overview of Scikit-Learn and Python.srt |
12.34KB |
| 8. Categorical Plots - Distributions within Categories - Coding with Seaborn.mp4 |
111.24MB |
| 8. Categorical Plots - Distributions within Categories - Coding with Seaborn.srt |
28.26KB |
| 8. Linear Regression - Scikit-Learn Train Test Split.mp4 |
82.93MB |
| 8. Linear Regression - Scikit-Learn Train Test Split.srt |
23.78KB |
| 8. Matplotlib Styling - Colors and Styles.mp4 |
81.19MB |
| 8. Matplotlib Styling - Colors and Styles.srt |
21.04KB |
| 8. Pandas - Conditional Filtering.mp4 |
90.04MB |
| 8. Pandas - Conditional Filtering.srt |
27.14KB |
| 9. Advanced Matplotlib Commands (Optional).mp4 |
40.44MB |
| 9. Advanced Matplotlib Commands (Optional).srt |
6.49KB |
| 9. Linear Regression - Scikit-Learn Performance Evaluation - Regression.mp4 |
73.64MB |
| 9. Linear Regression - Scikit-Learn Performance Evaluation - Regression.srt |
22.83KB |
| 9. Pandas - Useful Methods - Apply on Single Column.mp4 |
73.05MB |
| 9. Pandas - Useful Methods - Apply on Single Column.srt |
20.23KB |
| 9. Seaborn - Comparison Plots - Understanding the Plot Types.mp4 |
23.35MB |
| 9. Seaborn - Comparison Plots - Understanding the Plot Types.srt |
8.73KB |