MIT: Machine Learning with Python: from Linear Models to Deep Learning
An in-depth introduction to the field of machine learning, from linear models to deep learning and reinforcement learning, through hands-on Python projects. — Part of the MITx MicroMasters program in Statistics and Data Science.
✅ 15 weeks; 10-14 hours per week
✅ Instructor-paced – Instructor-led on a course schedule
✅ Free Limited Access Optional upgrade available.
Lectures :
✅ Introduction
✅ Linear classifiers, separability, perceptron algorithm
✅ Maximum margin hyperplane, loss, regularization
✅ Stochastic gradient descent, over-fitting, generalization
✅ Linear regression
✅ Recommender problems, collaborative filtering
✅ Non-linear classification, kernels
✅ Learning features, Neural networks
✅ Deep learning, back propagation
✅ Recurrent neural networks
✅ Generalization, complexity, VC-dimension
✅ Unsupervised learning: clustering
✅ Generative models, mixtures
✅ Mixtures and the EM algorithm
✅ Learning to control: Reinforcement learning
✅ Reinforcement learning continued
✅ Applications: Natural Language Processing
Projects :
✅ Automatic Review Analyzer
✅ Digit Recognition with Neural Networks
✅ Reinforcement Learning
