Stanford CS229 - Machine Learning Full Course Taught by Andrew Ng - Autumn 2018

Dive into a comprehensive machine learning course from Stanford University, taught by renowned AI expert Andrew Ng. Over 27 hours of lectures cover fundamental concepts and advanced topics in the field. Begin with an introduction to machine learning, then progress through linear regression, gradient descent, logistic regression, and generalized linear models. Explore support vector machines, kernels, and decision trees before delving into neural networks, including backpropagation and optimization techniques. Learn about expectation-maximization algorithms, factor analysis, and independent component analysis. Conclude with an in-depth look at reinforcement learning, covering Markov decision processes, value iteration, and model simulation. Gain practical skills in debugging machine learning models and conducting error analysis throughout the course.

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