First Section
2. 1-D Linear Regression Theory and Code
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1. Define the model in 1-D, derive the solution (Updated Version) (12:44)
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2. Define the model in 1-D, derive the solution (14:52)
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3. Coding the 1-D solution in Python (7:38)
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4. Exercise Theory vs. Code (1:19)
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5. Determine how good the model is - r-squared (5:51)
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6. R-squared in code (2:15)
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7. Demonstrating Moore's Law in Code (8:01)
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8. R-squared Quiz 1 (1:48)
3. Multiple linear regression and polynomial regression
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1. Define the multi-dimensional problem and derive the solution (Updated Version) (9:34)
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2. Define the multi-dimensional problem and derive the solution (17:07)
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3. How to solve multiple linear regression using only matrices (1:55)
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4. Coding the multi-dimensional solution in Python (7:29)
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5. Polynomial regression - extending linear regression (with Python code) (7:57)
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6. Predicting Systolic Blood Pressure from Age and Weight (5:46)
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7. R-squared Quiz 2 (2:06)
4. Practical machine learning issues
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1. What do all these letters mean (6:23)
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2. Interpreting the Weights (4:01)
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3. Generalization error, train and test sets (2:49)
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4. Generalization and Overfitting Demonstration in Code (7:32)
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5. Categorical inputs (5:21)
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6. One-Hot Encoding Quiz (2:08)
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7. Probabilistic Interpretation of Squared Error (5:15)
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8. L2 Regularization - Theory (4:22)
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9. L2 Regularization - Code (4:13)
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10. The Dummy Variable Trap (3:59)
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11. Gradient Descent Tutorial (4:30)
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12. Gradient Descent for Linear Regression (2:14)
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13. Bypass the Dummy Variable Trap with Gradient Descent (4:17)
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14. L1 Regularization - Theory (3:06)
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15. L1 Regularization - Code (4:25)
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16. L1 vs L2 Regularization (3:06)
5. Conclusion and Next Steps
6. Appendix
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1. What is the Appendix (2:48)
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3. Windows-Focused Environment Setup 2018 (20:20)
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4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow (17:33)
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5. How to Code by Yourself (part 1) (15:55)
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6. How to Code by Yourself (part 2) (9:23)
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7. How to Succeed in this Course (Long Version) (10:25)
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8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced (22:04)
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9. Proof that using Jupyter Notebook is the same as not using it (12:29)
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10. What order should I take your courses in (part 1) (11:19)
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11. What order should I take your courses in (part 2) (16:07)
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12. Python 2 vs Python 3 (4:38)
