Machine Learning

Resources

Articles

• A visual introduction to machine learning - R2D3

  • Part 1

  • Part 2 - Model Tuning and the Bias-Variance Tradeoff

• Exploring Supervised Machine Learning Algorithms - Vladyslav Miller

• K-Means Clustering - John Loeber

• Linear Algebra 101 for AI/ML - Backprop

  • Part 1

  • Part 2

• Machine Learning: a sound primer - Alan Franzoni

• Machine Learning Exercises in Python - John Wittenauer

  • Part 1 - Simple Linear Regression

  • Part 2 - Multivariate Linear Regression

  • Part 3 - Logistic Regression

  • Part 4 - Multivariate Logistic Regression

  • Part 5 - Neural Networks

  • Part 6 - Support Vector Machines

  • Part 7 - K-Means Clustering & PCA

  • Part 8 - Anomaly Detection & Recommendation

• Machine Learning in a Week - Per Harald Borgen

• Machine Learning in a Year - Per Harald Borgen

• Machine Learning is Fun! - Adam Geitgey

  • Part 1 - The world’s easiest introduction to Machine Learning

  • Part 2 - Using Machine Learning to generate Super Mario Maker levels

  • Part 3 - Deep Learning and Convolutional Neural Networks

  • Part 4 - Modern Face Recognition with Deep Learning

  • Part 5 - Language Translation with Deep Learning and the Magic of Sequences

  • Part 6 - How to do Speech Recognition with Deep Learning

  • Part 7 - Abusing Generative Adversarial Networks to Make 8-bit Pixel Art

  • Part 8 - How to Intentionally Trick Neural Networks

• Neural Networks - 3Blue1Brown

• Top 10 data mining algorithms in plain English - Ray Li

Books

• An Introduction to Statistical Learning - Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
• Introduction to Machine Learning Interviews Book - Chip Huyen
• Learning From Data - Yaser S. Abu-Mostafa, Malik Magdon-Ismail, Hsuan-Tien Lin
• Pattern Recognition and Machine Learning - Christopher M. Bishop
• Probabilistic Machine Learning - Kevin Patrick Murphy
• The Elements of Statistical Learning - Trevor Hastie, Robert Tibshirani, Jerome Friedman
• The Nature Of Code - Daniel Shiffman

  • Chapter 10: Neural Networks
• Think Bayes 2 - Allen B. Downey

Courses

• Learning From Data - California Institute of Technology

GitHub repositories

• Applied ML
• Awesome Machine Learning
• Landmark Papers in Machine Learning
• Machine Learning Engineering Open Book - Stas Bekman
• Machine Learning Research Papers
• ML Papers of the Week

Hacker News threads

• Daily practices for building AI/ML skills?
• Has anyone successfully pivoted from web dev to AI/ML development?
• How to pivot to a Machine Learning engineer?

Websites

• Machine Learning for Beginners - Microsoft (GitHub)
• Machine Learning Video Library - Professor Yaser Abu-Mostafa, California Institute of Technology
• ML Code Challenges

YouTube playlists

• Machine Learning - Andrew Ng
• Neural Networks - 3Blue1Brown
• Statistical Learning - Stanford