Stanford CS229: Machine Learning led by Andrew Ng | Autumn 2018 — 1d 3h 52m total, 21 videos

1×	1d 3h 51m 47s
1.25×	22h 17m 26s
1.5×	18h 34m 31s
1.75×	15h 55m 18s
2×	13h 55m 54s

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Videos (21)

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1	Stanford CS229: Machine Learning Lecture 1 - Andrew Ng (Autumn 2018)	1:15:20	2020-04-17
2	Stanford CS229: Machine Learning - Linear Regression and Gradient Descent \| Lecture 2 (Autumn 2018)	1:18:17	2020-04-17
3	Locally Weighted & Logistic Regression \| Stanford CS229: Machine Learning - Lecture 3 (Autumn 2018)	1:19:34	2020-04-17
4	Lecture 4 - Perceptron & Generalized Linear Model \| Stanford CS229: Machine Learning (Autumn 2018)	1:22:02	2020-04-17
5	Lecture 5 - GDA & Naive Bayes \| Stanford CS229: Machine Learning Andrew Ng (Autumn 2018)	1:18:52	2020-04-17
6	Lecture 6 - Support Vector Machines \| Stanford CS229: Machine Learning Andrew Ng (Autumn 2018)	1:20:57	2020-04-17
7	Lecture 7 - Kernels \| Stanford CS229: Machine Learning Andrew Ng (Autumn 2018)	1:20:25	2020-04-17
8	Lecture 8 - Data Splits, Models & Cross-Validation \| Stanford CS229: Machine Learning (Autumn 2018)	1:23:26	2020-04-17
9	Discussion Section: Learning Theory \| Stanford CS229: Machine Learning (Autumn 2018)	1:26:03	2020-04-17
10	Lecture 9 - Decision Trees and Ensemble Methods \| Stanford CS229: Machine Learning (Autumn 2018)	1:20:41	2020-04-17
11	Lecture 10 - Introduction to Neural Networks \| Stanford CS229: Machine Learning (Autumn 2018)	1:20:14	2020-04-17
12	Lecture 11 - Backprop & Improving Neural Networks \| Stanford CS229: Machine Learning (Autumn 2018)	1:16:38	2020-04-17
13	Lecture 12 - Debugging ML Models and Error Analysis \| Stanford CS229: Machine Learning (Autumn 2018)	1:18:55	2020-04-17
14	Lecture 13 - Expectation-Maximization Algorithms \| Stanford CS229: Machine Learning (Autumn 2018)	1:20:31	2020-04-17
15	Lecture 14 - EM Algorithm & Factor Analysis \| Stanford CS229: Machine Learning Andrew Ng -Autumn2018	1:19:48	2020-04-17
16	Lecture 15 - PCA and ICA \| Stanford CS229: Machine Learning Andrew Ng - Autumn 2018	1:18:35	2026-02-26
17	Lecture 16 - Independent Component Analysis & RL \| Stanford CS229: Machine Learning (Autumn 2018)	1:18:10	2020-04-17
18	Lecture 17 - MDPs & Value/Policy Iteration \| Stanford CS229: Machine Learning Andrew Ng (Autumn2018)	1:19:14	2020-04-17
19	Lecture 18 - Continous State MDP & Model Simulation \| Stanford CS229: Machine Learning (Autumn 2018)	1:20:15	2020-04-17
20	Lecture 19 - Reward Model & Linear Dynamical System \| Stanford CS229: Machine Learning (Autumn 2018)	1:21:07	2020-04-17
21	RL Debugging and Diagnostics \| Stanford CS229: Machine Learning Andrew Ng - Lecture 20 (Autumn 2018)	1:12:43	2020-04-17

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