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Deep Learning Prerequisites: Linear Regression in Python
Introduction and Outline
Introduction and Outline (7:41)
Special Offer! Get the VIP version of this course (1:14)
What is machine learning? How does linear regression play a role? (5:13)
Introduction to Moore's Law Problem (2:30)
How to Succeed in this Course (3:13)
1-D Linear Regression: Theory and Code
Define the model in 1-D, derive the solution (Updated Version) (12:43)
Define the model in 1-D, derive the solution (14:52)
Coding the 1-D solution in Python (7:38)
Determine how good the model is - r-squared (5:50)
R-squared in code (2:15)
Demonstrating Moore's Law in Code (8:00)
R-Squared Quiz
Multiple linear regression and polynomial regression
Define the multi-dimensional problem and derive the solution (Updated Version) (9:34)
Define the multi-dimensional problem and derive the solution (17:07)
How to solve multiple linear regression using only matrices (1:55)
Coding the multi-dimensional solution in Python (7:29)
Polynomial regression - extending linear regression (with Python code) (7:56)
Predicting Systolic Blood Pressure from Age and Weight (5:45)
R-Squared Quiz 2
Practical machine learning issues
What do all these letters mean? (6:23)
Interpreting the Weights (4:01)
Generalization error, train and test sets (2:49)
Generalization and Overfitting Demonstration in Code (7:32)
Categorical inputs (5:21)
One-hot encoding
Probabilistic Interpretation of Squared Error (5:15)
L2 Regularization - Theory (4:22)
L2 Regularization - Code (4:13)
The Dummy Variable Trap (3:58)
Gradient Descent Tutorial (4:30)
Gradient Descent for Linear Regression (2:13)
Bypass the Dummy Variable Trap with Gradient Descent (4:17)
L1 Regularization - Theory (3:05)
L1 Regularization - Code (4:25)
L1 vs L2 Regularization (3:06)
Conclusion and Next Steps
Brief overview of advanced linear regression and machine learning topics (5:15)
Exercises, practice, and how to get good at this (3:54)
Appendix
How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow (17:22)
How to Code by Yourself (part 1) (15:54)
How to Code by Yourself (part 2) (9:23)
How to Succeed in this Course (Long Version) (10:24)
BONUS: Where to get discount coupons and FREE deep learning material (5:31)
Determine how good the model is - r-squared
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