/MITx-6.86x-Machine-Learning

MITx 6.86x | Machine Learning with Python | From Linear Models to Deep Learning

Primary LanguageJupyter Notebook

MITx: Machine Learning

MITx: Statistics and Data Science MicroMasters

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

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.