Stanford CS229: Machine Learning Full Course taught by Andrew Ng | Autumn 2018
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What you'll learn
This course includes
- 27.5 hours of video
- Certificate of completion
- Access on mobile and TV
Course content
1 modules • 20 lessons • 27.5 hours of video
Stanford CS229: Machine Learning Full Course taught by Andrew Ng | Autumn 2018
20 lessons
• 26.5 hours
Stanford CS229: Machine Learning Full Course taught by Andrew Ng | Autumn 2018
20 lessons
• 26.5 hours
- Stanford CS229: Machine Learning - Linear Regression and Gradient Descent | Lecture 2 (Autumn 2018) 01:18:17
- Locally Weighted & Logistic Regression | Stanford CS229: Machine Learning - Lecture 3 (Autumn 2018) 01:19:34
- Lecture 4 - Perceptron & Generalized Linear Model | Stanford CS229: Machine Learning (Autumn 2018) 01:22:02
- Lecture 5 - GDA & Naive Bayes | Stanford CS229: Machine Learning Andrew Ng (Autumn 2018) 01:18:52
- Lecture 6 - Support Vector Machines | Stanford CS229: Machine Learning Andrew Ng (Autumn 2018) 01:20:57
- Lecture 7 - Kernels | Stanford CS229: Machine Learning Andrew Ng (Autumn 2018) 01:20:25
- Lecture 8 - Data Splits, Models & Cross-Validation | Stanford CS229: Machine Learning (Autumn 2018) 01:23:26
- Discussion Section: Learning Theory | Stanford CS229: Machine Learning (Autumn 2018) 01:26:03
- Lecture 9 - Decision Trees and Ensemble Methods | Stanford CS229: Machine Learning (Autumn 2018) 01:20:41
- Lecture 10 - Introduction to Neural Networks | Stanford CS229: Machine Learning (Autumn 2018) 01:20:14
- Lecture 11 - Backprop & Improving Neural Networks | Stanford CS229: Machine Learning (Autumn 2018) 01:16:38
- Lecture 12 - Debugging ML Models and Error Analysis | Stanford CS229: Machine Learning (Autumn 2018) 01:18:55
- Lecture 13 - Expectation-Maximization Algorithms | Stanford CS229: Machine Learning (Autumn 2018) 01:20:31
- Lecture 14 - EM Algorithm & Factor Analysis | Stanford CS229: Machine Learning Andrew Ng -Autumn2018 01:19:48
- Lecture 15 - PCA and ICA | Stanford CS229: Machine Learning Andrew Ng - Autumn 2018 01:18:35
- Lecture 16 - Independent Component Analysis & RL | Stanford CS229: Machine Learning (Autumn 2018) 01:18:10
- Lecture 17 - MDPs & Value/Policy Iteration | Stanford CS229: Machine Learning Andrew Ng (Autumn2018) 01:19:14
- Lecture 18 - Continous State MDP & Model Simulation | Stanford CS229: Machine Learning (Autumn 2018) 01:20:15
- Lecture 19 - Reward Model & Linear Dynamical System | Stanford CS229: Machine Learning (Autumn 2018) 01:21:07
- RL Debugging and Diagnostics | Stanford CS229: Machine Learning Andrew Ng - Lecture 20 (Autumn 2018) 01:12:43
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