Machine Learning (Stanford CS229)

Advanced Computer Science by Stanford Online. 21 lectures, 27.9 hours. Covers: machine learning supervised learning regression classification svm neural networks gradient descent.

Lectures

1. Stanford CS229: Machine Learning Lecture 1 - Andrew Ng (Autumn 2018) · 1 h 15 min
2. Stanford CS229: Machine Learning - Linear Regression and Gradient Descent | Lecture 2 (Autumn 2018) · 1 h 18 min
3. Locally Weighted & Logistic Regression | Stanford CS229: Machine Learning - Lecture 3 (Autumn 2018) · 1 h 20 min
4. Lecture 4 - Perceptron & Generalized Linear Model | Stanford CS229: Machine Learning (Autumn 2018) · 1 h 22 min
5. Lecture 5 - GDA & Naive Bayes | Stanford CS229: Machine Learning Andrew Ng (Autumn 2018) · 1 h 19 min
6. Lecture 6 - Support Vector Machines | Stanford CS229: Machine Learning Andrew Ng (Autumn 2018) · 1 h 21 min
7. Lecture 7 - Kernels | Stanford CS229: Machine Learning Andrew Ng (Autumn 2018) · 1 h 20 min
8. Lecture 8 - Data Splits, Models & Cross-Validation | Stanford CS229: Machine Learning (Autumn 2018) · 1 h 23 min
9. Discussion Section: Learning Theory | Stanford CS229: Machine Learning (Autumn 2018) · 1 h 26 min
10. Lecture 9 - Decision Trees and Ensemble Methods | Stanford CS229: Machine Learning (Autumn 2018) · 1 h 21 min
11. Lecture 10 - Introduction to Neural Networks | Stanford CS229: Machine Learning (Autumn 2018) · 1 h 20 min
12. Lecture 11 - Backprop & Improving Neural Networks | Stanford CS229: Machine Learning (Autumn 2018) · 1 h 17 min
13. Lecture 12 - Debugging ML Models and Error Analysis | Stanford CS229: Machine Learning (Autumn 2018) · 1 h 19 min
14. Lecture 13 - Expectation-Maximization Algorithms | Stanford CS229: Machine Learning (Autumn 2018) · 1 h 21 min
15. Lecture 14 - EM Algorithm & Factor Analysis | Stanford CS229: Machine Learning Andrew Ng -Autumn2018 · 1 h 20 min
16. Lecture 15 - PCA and ICA | Stanford CS229: Machine Learning Andrew Ng - Autumn 2018 · 1 h 19 min
17. Lecture 16 - Independent Component Analysis & RL | Stanford CS229: Machine Learning (Autumn 2018) · 1 h 18 min
18. Lecture 17 - MDPs & Value/Policy Iteration | Stanford CS229: Machine Learning Andrew Ng (Autumn2018) · 1 h 19 min
19. Lecture 18 - Continous State MDP & Model Simulation | Stanford CS229: Machine Learning (Autumn 2018) · 1 h 20 min
20. Lecture 19 - Reward Model & Linear Dynamical System | Stanford CS229: Machine Learning (Autumn 2018) · 1 h 21 min
21. RL Debugging and Diagnostics | Stanford CS229: Machine Learning Andrew Ng - Lecture 20 (Autumn 2018) · 1 h 13 min

Quick explainers

• Gradient Descent in 3 minutes · 3 min
• Gradient Descent, Step-by-Step · 24 min
• Gradient Descent Explained · 7 min
• Gradient descent, how neural networks learn | Deep Learning Chapter 2 · 21 min
• Machine Learning Crash Course: Gradient Descent · 2 min
• STOCHASTIC Gradient Descent (in 3 minutes) · 4 min
• How Gradient Descent Works. Simple Explanation · 5 min
• Gradient Descent - Simply Explained! ML for beginners with Code Example! · 13 min
• Intro to Gradient Descent || Optimizing High-Dimensional Equations · 11 min
• Stochastic Gradient Descent, Clearly Explained!!! · 11 min

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