6. One-Hot Encoding Quiz | The Course Tree

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Deep Learning Prerequisites Linear Regression in Python

First Section

1. Welcome (3:14)
2. Introduction and Outline (3:36)
3. What is machine learning How does linear regression play a role (5:13)
4. Introduction to Moore's Law Problem (2:30)
6. How to Succeed in this Course (3:13)

2. 1-D Linear Regression Theory and Code

1. Define the model in 1-D, derive the solution (Updated Version) (12:44)
2. Define the model in 1-D, derive the solution (14:52)
3. Coding the 1-D solution in Python (7:38)
4. Exercise Theory vs. Code (1:19)
5. Determine how good the model is - r-squared (5:51)
6. R-squared in code (2:15)
7. Demonstrating Moore's Law in Code (8:01)
8. R-squared Quiz 1 (1:48)

3. Multiple linear regression and polynomial regression

1. Define the multi-dimensional problem and derive the solution (Updated Version) (9:34)
2. Define the multi-dimensional problem and derive the solution (17:07)
3. How to solve multiple linear regression using only matrices (1:55)
4. Coding the multi-dimensional solution in Python (7:29)
5. Polynomial regression - extending linear regression (with Python code) (7:57)
6. Predicting Systolic Blood Pressure from Age and Weight (5:46)
7. R-squared Quiz 2 (2:06)

4. Practical machine learning issues

1. What do all these letters mean (6:23)
2. Interpreting the Weights (4:01)
3. Generalization error, train and test sets (2:49)
4. Generalization and Overfitting Demonstration in Code (7:32)
5. Categorical inputs (5:21)
6. One-Hot Encoding Quiz (2:08)
7. Probabilistic Interpretation of Squared Error (5:15)
8. L2 Regularization - Theory (4:22)
9. L2 Regularization - Code (4:13)
10. The Dummy Variable Trap (3:59)
11. Gradient Descent Tutorial (4:30)
12. Gradient Descent for Linear Regression (2:14)
13. Bypass the Dummy Variable Trap with Gradient Descent (4:17)
14. L1 Regularization - Theory (3:06)
15. L1 Regularization - Code (4:25)
16. L1 vs L2 Regularization (3:06)

5. Conclusion and Next Steps

1. Brief overview of advanced linear regression and machine learning topics (5:15)
2. Exercises, practice, and how to get good at this (3:54)

6. Appendix

1. What is the Appendix (2:48)
3. Windows-Focused Environment Setup 2018 (20:20)
4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow (17:33)
5. How to Code by Yourself (part 1) (15:55)
6. How to Code by Yourself (part 2) (9:23)
7. How to Succeed in this Course (Long Version) (10:25)
8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced (22:04)
9. Proof that using Jupyter Notebook is the same as not using it (12:29)
10. What order should I take your courses in (part 1) (11:19)
11. What order should I take your courses in (part 2) (16:07)
12. Python 2 vs Python 3 (4:38)

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