MITx: Machine Learning

These are my notes taken during the course MITx 6.86x "Machine Learning with Python: From Linear Models to Deep Learning", in Sept-Dec 2021.

Unit 1 | Linear Classifiers and Generalizations

• Lecture 01. Introduction to Machine Learning
• Lecture 02. Linear Classifier and Perceptron
• Lecture 03. Hinge loss, Margin boundaries and Regularization
• Lecture 04. Linear Classification and Generalization

1. Project 1: Automatic Review Analyzer

Unit 2 | Nonlinear Classification, Linear regression, Collaborative Filtering

• Lecture 05. Linear Regression 9 of 9 possible points (9/9) 100%
• Lecture 06. Nonlinear Classification
• Lecture 07. Recommender Systems

Project 2: Digit recognition (part 1)

Unit 3 | Neural networks

• Lecture 08. Introduction to Feedforward Neural Networks
• Lecture 09. Feedforward Neural Networks, Back Propagation, and Stochastic Gradient Descent (SGD)
• Lecture 10. Recurrent Neural Networks 1
• Lecture 11. Recurrent Neural Networks 2
• Lecture 12. Convolutional Neural Networks

Project 3: Digit recognition (part 2)

Unit 4 | Un-supervised Learning

• Lecture 13. Clustering 1
• Lecture 14. Clustering 2
• Lecture 15. Generative Models
• Lecture 16. Mixture Models; EM algorithm

Project 4: Collaborative Filtering via Gaussian Mixtures

Unit 5 | Re-inforcement Learning

• Lecture 17. Reinforcement Learning 1
• Lecture 18. Reinforcement Learning 2
• Lecture 19: Applications: Natural Language Processing

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Link: https://learning.edx.org/course/course-v1:MITx+6.86x+3T2021/home

Note: This repo includes code prepared by the course staff.
Note: Please inform me if I forgot to remove some answers to the graded problems.