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文档列表

5. Pandas/28. Pandas Project Exercise Solutions.mp4 190.4 MB
8. Data Analysis and Visualization Capstone Project Exercise/4. Capstone Project Solutions - Part Three.mp4 150.9 MB
5. Pandas/26. Pandas Pivot Tables.mp4 135.0 MB
7. Seaborn Data Visualizations/2. Scatterplots with Seaborn.mp4 134.9 MB
6. Matplotlib/11. Matplotlib Exercise Questions - Solutions.mp4 129.1 MB
8. Data Analysis and Visualization Capstone Project Exercise/2. Capstone Project Solutions - Part One.mp4 122.6 MB
5. Pandas/4. DataFrames - Part One - Creating a DataFrame.mp4 119.6 MB
7. Seaborn Data Visualizations/8. Categorical Plots - Distributions within Categories - Coding with Seaborn.mp4 116.6 MB
8. Data Analysis and Visualization Capstone Project Exercise/3. Capstone Project Solutions - Part Two.mp4 116.4 MB
7. Seaborn Data Visualizations/14. Seaborn Plot Exercises Solutions.mp4 116.0 MB
4. NumPy/2. NumPy Arrays.mp4 115.0 MB
5. Pandas/23. Pandas Input and Output - HTML Tables.mp4 111.8 MB
5. Pandas/15. GroupBy Operations - Part Two - MultiIndex.mp4 111.0 MB
5. Pandas/25. Pandas Input and Output - SQL Databases.mp4 108.2 MB
5. Pandas/21. Pandas - Time Methods for Date and Time Data.mp4 106.9 MB
10. Linear Regression/24. L1 Regularization - Lasso Regression - Background and Implementation.mp4 104.9 MB
1. Introduction to Course/3. Anaconda Python and Jupyter Install and Setup.mp4 103.6 MB
5. Pandas/10. Pandas - Useful Methods - Apply on Multiple Columns.mp4 103.3 MB
5. Pandas/13. Missing Data - Pandas Operations.mp4 102.6 MB
5. Pandas/7. DataFrames - Part Four - Working with Rows.mp4 101.4 MB
10. Linear Regression/23. L2 Regularization - Ridge Regression - Python Implementation.mp4 101.1 MB
6. Matplotlib/6. Matplotlib - Subplots Functionality.mp4 100.9 MB
10. Linear Regression/25. L1 and L2 Regularization - Elastic Net.mp4 97.9 MB
8. Data Analysis and Visualization Capstone Project Exercise/1. Capstone Project Overview.mp4 97.7 MB
5. Pandas/14. GroupBy Operations - Part One.mp4 97.6 MB
10. Linear Regression/6. Python coding Simple Linear Regression.mp4 96.4 MB
7. Seaborn Data Visualizations/11. Seaborn Grid Plots.mp4 96.1 MB
5. Pandas/8. Pandas - Conditional Filtering.mp4 94.4 MB
5. Pandas/6. DataFrames - Part Three - Working with Columns.mp4 93.6 MB
10. Linear Regression/11. Linear Regression - Model Deployment and Coefficient Interpretation.mp4 92.5 MB
10. Linear Regression/3. Linear Regression - Understanding Ordinary Least Squares.mp4 90.4 MB
5. Pandas/11. Pandas - Useful Methods - Statistical Information and Sorting.mp4 89.8 MB
10. Linear Regression/8. Linear Regression - Scikit-Learn Train Test Split.mp4 87.0 MB
6. Matplotlib/8. Matplotlib Styling - Colors and Styles.mp4 85.1 MB
7. Seaborn Data Visualizations/4. Distribution Plots - Part Two - Coding with Seaborn.mp4 81.5 MB
5. Pandas/20. Pandas - Text Methods for String Data.mp4 79.4 MB
10. Linear Regression/9. Linear Regression - Scikit-Learn Performance Evaluation - Regression.mp4 77.2 MB
5. Pandas/9. Pandas - Useful Methods - Apply on Single Column.mp4 76.6 MB
10. Linear Regression/16. Polynomial Regression - Choosing Degree of Polynomial.mp4 76.5 MB
9. Machine Learning Concepts Overview/4. Supervised Machine Learning Process.mp4 74.9 MB
7. Seaborn Data Visualizations/12. Seaborn - Matrix Plots.mp4 74.7 MB
7. Seaborn Data Visualizations/10. Seaborn - Comparison Plots - Coding with Seaborn.mp4 73.6 MB
10. Linear Regression/5. Linear Regression - Gradient Descent.mp4 68.2 MB
10. Linear Regression/20. Introduction to Cross Validation.mp4 65.6 MB
7. Seaborn Data Visualizations/7. Categorical Plots - Distributions within Categories - Understanding Plot Types.mp4 64.1 MB
10. Linear Regression/22. L2 Regularization - Ridge Regression Theory.mp4 64.1 MB
10. Linear Regression/10. Linear Regression - Residual Plots.mp4 62.4 MB
6. Matplotlib/4. Matplotlib - Implementing Figures and Axes.mp4 62.0 MB
7. Seaborn Data Visualizations/6. Categorical Plots - Statistics within Categories - Coding with Seaborn.mp4 57.7 MB
10. Linear Regression/2. Linear Regression - Algorithm History.mp4 57.4 MB
10. Linear Regression/19. Feature Scaling.mp4 56.6 MB
5. Pandas/5. DataFrames - Part Two - Basic Properties.mp4 56.5 MB
6. Matplotlib/2. Matplotlib Basics.mp4 56.2 MB
5. Pandas/17. Combining DataFrames - Inner Merge.mp4 56.2 MB
5. Pandas/12. Missing Data - Overview.mp4 55.8 MB
10. Linear Regression/13. Polynomial Regression - Creating Polynomial Features.mp4 55.2 MB
6. Matplotlib/10. Matplotlib Exercise Questions Overview.mp4 53.2 MB
5. Pandas/16. Combining DataFrames - Concatenation.mp4 53.0 MB
7. Seaborn Data Visualizations/13. Seaborn Plot Exercises Overview.mp4 52.3 MB
5. Pandas/22. Pandas Input and Output - CSV Files.mp4 52.3 MB
1. Introduction to Course/4. Environment Setup.mp4 51.7 MB
10. Linear Regression/14. Polynomial Regression - Training and Evaluation.mp4 51.2 MB
4. NumPy/5. NumPy Operations.mp4 50.9 MB
4. NumPy/7. Numpy Exercises - Solutions.mp4 50.9 MB
4. NumPy/4. NumPy Indexing and Selection.mp4 48.6 MB
10. Linear Regression/7. Overview of Scikit-Learn and Python.mp4 47.8 MB
5. Pandas/3. Series - Part Two.mp4 47.5 MB
9. Machine Learning Concepts Overview/2. Why Machine Learning.mp4 46.9 MB
10. Linear Regression/12. Polynomial Regression - Theory and Motivation.mp4 46.6 MB
10. Linear Regression/15. Bias Variance Trade-Off.mp4 45.1 MB
5. Pandas/27. Pandas Project Exercise Overview.mp4 43.1 MB
3. Machine Learning Pathway Overview/1. Machine Learning Pathway.mp4 42.5 MB
6. Matplotlib/9. Advanced Matplotlib Commands (Optional).mp4 42.4 MB
5. Pandas/19. Combining DataFrames - Outer Merge.mp4 41.8 MB
10. Linear Regression/26. Linear Regression Project - Data Overview.mp4 41.0 MB
9. Machine Learning Concepts Overview/3. Types of Machine Learning Algorithms.mp4 40.6 MB
5. Pandas/2. Series - Part One.mp4 40.3 MB
10. Linear Regression/4. Linear Regression - Cost Functions.mp4 37.8 MB
5. Pandas/24. Pandas Input and Output - Excel Files.mp4 36.3 MB
10. Linear Regression/21. Regularization Data Setup.mp4 36.1 MB
6. Matplotlib/7. Matplotlib Styling - Legends.mp4 35.8 MB
10. Linear Regression/18. Regularization Overview.mp4 35.0 MB
7. Seaborn Data Visualizations/3. Distribution Plots - Part One - Understanding Plot Types.mp4 33.6 MB
9. Machine Learning Concepts Overview/1. Introduction to Machine Learning Overview Section.mp4 31.2 MB
2. OPTIONAL Python Crash Course/2. Python Crash Course - Part One.mp4 31.0 MB
10. Linear Regression/17. Polynomial Regression - Model Deployment.mp4 30.3 MB
5. Pandas/18. Combining DataFrames - Left and Right Merge.mp4 29.3 MB
1. Introduction to Course/3.1 UNZIP_ME_FOR_NOTEBOOKS.zip 28.4 MB
1. Introduction to Course/2.1 UNZIP_ME_FOR_NOTEBOOKS.zip 28.4 MB
6. Matplotlib/3. Matplotlib - Understanding the Figure Object.mp4 27.1 MB
2. OPTIONAL Python Crash Course/6. Python Crash Course - Exercise Solutions.mp4 26.3 MB
1. Introduction to Course/2. COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP!.mp4 25.7 MB
6. Matplotlib/5. Matplotlib - Figure Parameters.mp4 24.9 MB
7. Seaborn Data Visualizations/9. Seaborn - Comparison Plots - Understanding the Plot Types.mp4 24.5 MB
2. OPTIONAL Python Crash Course/4. Python Crash Course - Part Three.mp4 24.3 MB
2. OPTIONAL Python Crash Course/3. Python Crash Course - Part Two.mp4 23.3 MB
7. Seaborn Data Visualizations/5. Categorical Plots - Statistics within Categories - Understanding Plot Types.mp4 22.9 MB
6. Matplotlib/1. Introduction to Matplotlib.mp4 22.6 MB
5. Pandas/1. Introduction to Pandas.mp4 22.0 MB
7. Seaborn Data Visualizations/1. Introduction to Seaborn.mp4 21.0 MB
9. Machine Learning Concepts Overview/5. Companion Book - Introduction to Statistical Learning.mp4 20.2 MB
4. NumPy/6. NumPy Exercises.mp4 12.1 MB
4. NumPy/1. Introduction to NumPy.mp4 11.8 MB
10. Linear Regression/1. Introduction to Linear Regression Section.mp4 9.3 MB
2. OPTIONAL Python Crash Course/5. Python Crash Course - Exercise Questions.mp4 5.2 MB
5. Pandas/28. Pandas Project Exercise Solutions.srt 39.7 kB
5. Pandas/26. Pandas Pivot Tables.srt 32.9 kB
4. NumPy/2. NumPy Arrays.srt 32.7 kB
5. Pandas/21. Pandas - Time Methods for Date and Time Data.srt 32.5 kB
8. Data Analysis and Visualization Capstone Project Exercise/4. Capstone Project Solutions - Part Three.srt 31.6 kB
7. Seaborn Data Visualizations/2. Scatterplots with Seaborn.srt 30.4 kB
5. Pandas/25. Pandas Input and Output - SQL Databases.srt 30.1 kB
5. Pandas/4. DataFrames - Part One - Creating a DataFrame.srt 29.7 kB
6. Matplotlib/6. Matplotlib - Subplots Functionality.srt 29.3 kB
7. Seaborn Data Visualizations/8. Categorical Plots - Distributions within Categories - Coding with Seaborn.srt 28.9 kB
10. Linear Regression/6. Python coding Simple Linear Regression.srt 28.8 kB
5. Pandas/13. Missing Data - Pandas Operations.srt 28.1 kB
5. Pandas/8. Pandas - Conditional Filtering.srt 27.8 kB
8. Data Analysis and Visualization Capstone Project Exercise/2. Capstone Project Solutions - Part One.srt 27.5 kB
10. Linear Regression/23. L2 Regularization - Ridge Regression - Python Implementation.srt 27.1 kB
5. Pandas/10. Pandas - Useful Methods - Apply on Multiple Columns.srt 26.6 kB
10. Linear Regression/25. L1 and L2 Regularization - Elastic Net.srt 26.3 kB
10. Linear Regression/11. Linear Regression - Model Deployment and Coefficient Interpretation.srt 26.2 kB
7. Seaborn Data Visualizations/4. Distribution Plots - Part Two - Coding with Seaborn.srt 25.4 kB
2. OPTIONAL Python Crash Course/2. Python Crash Course - Part One.srt 25.2 kB
6. Matplotlib/11. Matplotlib Exercise Questions - Solutions.srt 25.1 kB
5. Pandas/20. Pandas - Text Methods for String Data.srt 24.5 kB
10. Linear Regression/8. Linear Regression - Scikit-Learn Train Test Split.srt 24.3 kB
8. Data Analysis and Visualization Capstone Project Exercise/3. Capstone Project Solutions - Part Two.srt 24.0 kB
5. Pandas/11. Pandas - Useful Methods - Statistical Information and Sorting.srt 24.0 kB
10. Linear Regression/9. Linear Regression - Scikit-Learn Performance Evaluation - Regression.srt 23.4 kB
10. Linear Regression/3. Linear Regression - Understanding Ordinary Least Squares.srt 23.1 kB
10. Linear Regression/24. L1 Regularization - Lasso Regression - Background and Implementation.srt 23.0 kB
7. Seaborn Data Visualizations/14. Seaborn Plot Exercises Solutions.srt 22.9 kB
5. Pandas/23. Pandas Input and Output - HTML Tables.srt 22.9 kB
1. Introduction to Course/3. Anaconda Python and Jupyter Install and Setup.srt 22.1 kB
5. Pandas/14. GroupBy Operations - Part One.srt 21.9 kB
7. Seaborn Data Visualizations/12. Seaborn - Matrix Plots.srt 21.6 kB
5. Pandas/7. DataFrames - Part Four - Working with Rows.srt 21.6 kB
6. Matplotlib/8. Matplotlib Styling - Colors and Styles.srt 21.5 kB
6. Matplotlib/4. Matplotlib - Implementing Figures and Axes.srt 21.5 kB
5. Pandas/15. GroupBy Operations - Part Two - MultiIndex.srt 21.4 kB
10. Linear Regression/22. L2 Regularization - Ridge Regression Theory.srt 21.2 kB
5. Pandas/6. DataFrames - Part Three - Working with Columns.srt 21.1 kB
8. Data Analysis and Visualization Capstone Project Exercise/1. Capstone Project Overview.srt 21.1 kB
7. Seaborn Data Visualizations/11. Seaborn Grid Plots.srt 21.0 kB
5. Pandas/9. Pandas - Useful Methods - Apply on Single Column.srt 20.7 kB
10. Linear Regression/10. Linear Regression - Residual Plots.srt 20.7 kB
7. Seaborn Data Visualizations/7. Categorical Plots - Distributions within Categories - Understanding Plot Types.srt 20.6 kB
10. Linear Regression/16. Polynomial Regression - Choosing Degree of Polynomial.srt 20.4 kB
10. Linear Regression/20. Introduction to Cross Validation.srt 20.3 kB
9. Machine Learning Concepts Overview/4. Supervised Machine Learning Process.srt 20.2 kB
6. Matplotlib/2. Matplotlib Basics.srt 20.1 kB
5. Pandas/17. Combining DataFrames - Inner Merge.srt 19.0 kB
5. Pandas/12. Missing Data - Overview.srt 18.8 kB
2. OPTIONAL Python Crash Course/3. Python Crash Course - Part Two.srt 18.5 kB
10. Linear Regression/5. Linear Regression - Gradient Descent.srt 17.1 kB
5. Pandas/22. Pandas Input and Output - CSV Files.srt 17.0 kB
2. OPTIONAL Python Crash Course/4. Python Crash Course - Part Three.srt 17.0 kB
10. Linear Regression/13. Polynomial Regression - Creating Polynomial Features.srt 16.8 kB
4. NumPy/4. NumPy Indexing and Selection.srt 16.6 kB
10. Linear Regression/15. Bias Variance Trade-Off.srt 16.3 kB
3. Machine Learning Pathway Overview/1. Machine Learning Pathway.srt 16.2 kB
7. Seaborn Data Visualizations/10. Seaborn - Comparison Plots - Coding with Seaborn.srt 16.1 kB
5. Pandas/3. Series - Part Two.srt 15.7 kB
5. Pandas/16. Combining DataFrames - Concatenation.srt 15.4 kB
7. Seaborn Data Visualizations/3. Distribution Plots - Part One - Understanding Plot Types.srt 15.4 kB
10. Linear Regression/19. Feature Scaling.srt 15.2 kB
9. Machine Learning Concepts Overview/2. Why Machine Learning.srt 15.0 kB
7. Seaborn Data Visualizations/6. Categorical Plots - Statistics within Categories - Coding with Seaborn.srt 15.0 kB
5. Pandas/19. Combining DataFrames - Outer Merge.srt 14.9 kB
1. Introduction to Course/4. Environment Setup.srt 14.8 kB
10. Linear Regression/14. Polynomial Regression - Training and Evaluation.srt 14.5 kB
2. OPTIONAL Python Crash Course/6. Python Crash Course - Exercise Solutions.srt 13.8 kB
5. Pandas/2. Series - Part One.srt 13.7 kB
5. Pandas/5. DataFrames - Part Two - Basic Properties.srt 13.6 kB
10. Linear Regression/2. Linear Regression - Algorithm History.srt 13.4 kB
10. Linear Regression/21. Regularization Data Setup.srt 12.7 kB
10. Linear Regression/7. Overview of Scikit-Learn and Python.srt 12.6 kB
4. NumPy/5. NumPy Operations.srt 12.3 kB
9. Machine Learning Concepts Overview/3. Types of Machine Learning Algorithms.srt 11.9 kB
6. Matplotlib/3. Matplotlib - Understanding the Figure Object.srt 11.8 kB
10. Linear Regression/4. Linear Regression - Cost Functions.srt 11.7 kB
7. Seaborn Data Visualizations/13. Seaborn Plot Exercises Overview.srt 11.5 kB
10. Linear Regression/12. Polynomial Regression - Theory and Motivation.srt 11.5 kB
5. Pandas/24. Pandas Input and Output - Excel Files.srt 11.1 kB
4. NumPy/7. Numpy Exercises - Solutions.srt 11.1 kB
6. Matplotlib/7. Matplotlib Styling - Legends.srt 10.6 kB
10. Linear Regression/18. Regularization Overview.srt 10.6 kB
5. Pandas/27. Pandas Project Exercise Overview.srt 9.8 kB
6. Matplotlib/10. Matplotlib Exercise Questions Overview.srt 9.6 kB
5. Pandas/18. Combining DataFrames - Left and Right Merge.srt 9.3 kB
7. Seaborn Data Visualizations/5. Categorical Plots - Statistics within Categories - Understanding Plot Types.srt 9.0 kB
7. Seaborn Data Visualizations/9. Seaborn - Comparison Plots - Understanding the Plot Types.srt 8.9 kB
9. Machine Learning Concepts Overview/1. Introduction to Machine Learning Overview Section.srt 8.8 kB
10. Linear Regression/17. Polynomial Regression - Model Deployment.srt 8.6 kB
10. Linear Regression/26. Linear Regression Project - Data Overview.srt 7.9 kB
6. Matplotlib/5. Matplotlib - Figure Parameters.srt 7.8 kB
5. Pandas/1. Introduction to Pandas.srt 7.4 kB
1. Introduction to Course/2. COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP!.srt 7.3 kB
6. Matplotlib/1. Introduction to Matplotlib.srt 6.9 kB
7. Seaborn Data Visualizations/1. Introduction to Seaborn.srt 6.7 kB
6. Matplotlib/9. Advanced Matplotlib Commands (Optional).srt 6.6 kB
9. Machine Learning Concepts Overview/5. Companion Book - Introduction to Statistical Learning.srt 4.8 kB
4. NumPy/1. Introduction to NumPy.srt 3.1 kB
10. Linear Regression/1. Introduction to Linear Regression Section.srt 2.7 kB
2. OPTIONAL Python Crash Course/5. Python Crash Course - Exercise Questions.srt 2.6 kB
4. NumPy/6. NumPy Exercises.srt 2.1 kB
1. Introduction to Course/1. EARLY BIRD INFO.html 550 Bytes
2. OPTIONAL Python Crash Course/1. OPTIONAL Python Crash Course.html 472 Bytes
1. Introduction to Course/4.2 requirements.txt 221 Bytes
4. NumPy/3. Coding Exercise Check-in Creating NumPy Arrays.html 163 Bytes
1. Introduction to Course/4.1 Backup Google Link for requirements.txt file.html 143 Bytes
[FreeCourseSite.com].url 127 Bytes

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