- 10.16 Intro to ML
- 10.23 Math and Coding Overview
- 10.30 Linear Regression: Theory
- 11.06 Linear Regression: Code
- 11.13 Logistic Regression: Theory
- 11.20 Logistic Regression: Code
- 11.27 Neural Network Introduction
- A great intro to gradient descent. The links included are super helpful and are also linked below for your covenience.
- Google's machine learning crash course. From interactive examples to clear explanations, Google's ML course is a great resource to understand or recap material.
- An example of linear regression using gradient descent. Helpful to see a real example of code implementing what we're trying to do. Week 4's code will be fairly similar.
- An online 3-D grapher. Plot functions, then calculate the gradient at certain points and locate the corresponding points on the graph. Check that if you move in the direction of the gradient, you're moving in the direction of steepest ascent. Now negate the gradient. Check that moving in the direction of the negated gradient causes you to move in the direction of steepest descent.
- Codecademy intro course. Learn Python in an interactive manner. Kind of slow if you have previous coding experience, but great for beginners and overall helpful for everyone. Make sure you practice the skills / concepts taught in larger project settings on your own to make sure you're comfortable with them.