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Item Details | Price |
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Instructor: Sahir
Validity Period: Lifetime
1. Course Introduction | |||
1 3:00 | |||
2. Environment Set-Up | |||
1. Environment Set-Up 8:00 | |||
3. Jupyter Overview | |||
2. Jupyter Notebooks_x264 13:00 | |||
3. Optional Virtual Environments 9:00 | |||
Python Crash Course | |||
._2. Introduction to Python Crash Course 1:00 | |||
3. Python Crash Course - Part 1 19:00 | |||
4. Python Crash Course - Part 2_x264 15:00 | |||
5. Python Crash Course - Part 3 16:00 | |||
6. Python Crash Course - Part 4 15:00 | |||
7. Python Crash Course Exercises - Overview_x264 3:00 | |||
8. Python Crash Course Exercises - Solutions 12:00 | |||
5. Python for Data Analysis - NumPy | |||
._2. Introduction to Numpy_x264 2:00 | |||
3. Numpy Arrays_x264 16:00 | |||
5. Numpy Array Indexing_x264 18:00 | |||
6. Numpy Operations_x264 7:00 | |||
7. Numpy Exercises Overview_x264 2:00 | |||
8. Numpy Exercises Solutions_x264 15:00 | |||
6. Python for Data Analysis - Pandas | |||
2. Introduction to Pandas_x264 1:00 | |||
3. Series_x264 10:00 | |||
4. DataFrames - Part 1_x264 15:00 | |||
5. DataFrames - Part 2_x264 17:00 | |||
6. DataFrames - Part 3_x264 9:00 | |||
7. Missing Data_x264 6:00 | |||
8. Groupby_x264 7:00 | |||
9. Merging Joining and Concatenating_x264 9:00 | |||
10. Operations_x264 13:00 | |||
11. Data Input and Output_x264 14:00 | |||
7. Python for Data Analysis - Pandas Exercises | |||
1. SF Salaries Exercise Overview_x264 1:00 | |||
3. SF Salaries Solutions_x264 15:00 | |||
4. Ecommerce Purchases Exercise Overview_x264 2:00 | |||
5. Ecommerce Purchases Exercise Solutions_x264 15:00 | |||
8. Python for Data Visualization - Matplotlib | |||
2. Introduction to Matplotlib_x264 3:00 | |||
3. Matplotlib Part 1_x264 17:00 | |||
4. Matplotlib Part 2_x264 15:00 | |||
5. Matplotlib Part 3_x264 11:00 | |||
6. Matplotlib Exercises Overview_x264 1:00 | |||
7. Matplotlib Exercises - Solutions_x264 10:00 | |||
9. Python for Data Visualization - Seaborn | |||
1. Introduction to Seaborn_x264 3:00 | |||
2. Distribution Plots_x264 18:00 | |||
3. Categorical Plots_x264 17:00 | |||
4. Matrix Plots_x264 10:00 | |||
5. Grids_x264 8:00 | |||
6. Regression Plots_x264 7:00 | |||
7. Style and Color_x264 8:00 | |||
8. Seaborn Exercise Overview_x264 2:00 | |||
9. Seaborn Exercise Solutions_x264 7:00 | |||
13. Data Capstone Project | |||
2. 911 Calls Project Overview_x264 1:00 | |||
3. 911 Calls Solutions - Part 1_x264 14:00 | |||
4. 911 Calls Solutions - Part 2_x264 19:00 | |||
6. Finance Data Project Overview_x264 3:00 | |||
7. Finance Project - Solutions Part 1_x264 17:00 | |||
8. Finance Project - Solutions Part 2_x264 18:00 | |||
9. Finance Project - Solutions Part 3_x264 6:00 | |||
14. Introduction to Machine Learning | |||
1. Welcome to the Machine Learning Section-FMT | |||
3. Introduction to Machine Learning_x264 10:00 | |||
4. Machine Learning with Python_x264 9:00 | |||
15. Linear Regression | |||
1. Linear Regression Theory_x264 4:00 | |||
3. Linear Regression with Python - Part 1_x264 18:00 | |||
4. Linear Regression with Python - Part 2_x264 7:00 | |||
5. Linear Regression Project Overview_x264 2:00 | |||
6. Linear Regression Project Solution_x264 18:00 | |||
16. Cross Validation and Bias-Variance Trade-Off | |||
1. Bias Variance Trade-Off_x264 6:00 | |||
17. Logistic Regression | |||
1.Logistic Regression Theory_264 10:00 | |||
2. Logistic Regression with Python - Part 1_x264 14:00 | |||
3. Logistic Regression with Python - Part 2_x264 16:00 | |||
4. Logistic Regression with Python - Part 3_x264 7:00 | |||
5. Logistic Regression Project Overview_x264 1:00 | |||
6. Logistic Regression Project Solutions_x264 11:00 | |||
18. K Nearest Neighbors | |||
1. KNN Theory_x264 5:00 | |||
2. KNN with Python_x264 19:00 | |||
3. KNN Project Overview_x264 1:00 | |||
4. KNN Project Solutions_x264 14:00 | |||
19. Decision Trees and Random Forests | |||
1. Introduction to Tree Methods_x264 6:00 | |||
2. Decision Trees and Random Forest with Python_x264 14:00 | |||
3. Decision Trees and Random Forest Project Overview_x264 3:00 | |||
4. Decision Trees and Random Forest Solutions Part 1_x264 12:00 | |||
5. Decision Trees and Random Forest Solutions Part 2_x264 9:00 | |||
20. Support Vector Machines | |||
1. SVM Theory_x264 4:00 | |||
2. Support Vector Machines with Python_x264 18:00 | |||
3. SVM Project Overview_x264 2:00 | |||
4. SVM Project Solutions_x264 10:00 | |||
21. K Means Clustering | |||
1. K Means Algorithm Theory_x264 5:00 | |||
2. K Means with Python_x264 12:00 | |||
3. K Means Project Overview_x264 3:00 | |||
4. K Means Project Solutions_x264 17:00 | |||
22. Principal Component Analysis | |||
1. Principal Component Analysis_x264 3:00 | |||
2. PCA with Python_x264 16:00 |
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