Awesome Full Stack Machine Learning Engineering Courses

This is curated list of publicly accessible machine learning courses from top universities such as Berkeley, Harvard, Stanford, and MIT. It also includes machine learning project case studies from large and experienced companies. The list is broken down by topics and areas of specializations. Python is the preferred language of choice as it covers end-to-end machine learning engineering.

Special thanks to the schools to make their course videos and assignments publicly available.

TL;DR

Bare minimum list of courses to go through for basic knowledge in machine learning engineering.

MIT: The Missing Sememster of Your CS Education

edX Harvard: CS50x: Introduction to Computer Science

MIT 18.05: Introduction to Probability and Statistics

Columbia COMS W4995: Applied Machine Learning 📺

MIT 18.06: Linear Algebra

Berkeley CS182: Designing, Visualizing, and Understanding Deep Neural Networks: 📺 [Reference Solutions]

Berkeley: Full Stack Deep Learning

Computer Science

Foundational computer science, Python, and SQL skills for machine learning engineering.

📚 Textbooks

Grokking Algorithms

Google Python Style Guide

Python Design Patterns

Python3 Patterns

Design Patterns: Elements of Reusable Object-Oriented Software 1st Edition

🏫 Courses

MIT: The Missing Sememster of Your CS Education ⭐

edX MITX: Introduction to Computer Science and Programming Using Python ⭐

edX Harvard: CS50x: Introduction to Computer Science

SQL for Data Analysis

PostgreSQL Exercises

U Waterloo: CS794: Optimization for Data Science

Berkeley CS 170: Efficient Algorithms and Intractable Problems

Berkeley CS 294-165: Sketching Algorithms

MIT 6.824: Distributed Systems 📺

Math and Statistics

Linear algebra and statistics

📚 Textbooks

NIST Engineering Statistics Handbook

🏫 Courses

MIT 18.05: Introduction to Probability and Statistics ⭐

MIT 18.06: Linear Algebra ⭐

Stanford Stats216: Statiscal Learning ⭐

CalTech: Learning From Data

A Students Guide to Bayesian Statistics

Introduction to Linear Algebra for Applied Machine Learning with Python

Artificial Intelligence

Artificial Intelligence is the superset of Machine Learning. These courses provides a much higher level understanding of the field of AI, including searching, planning, logic, constrain optimization, and machine learning.

📚 Textbooks

Artificial Intelligence: A Modern Approach

🏫 Courses

Berkeley CS188: Artificial Intelligence ⭐

edX ColumbiaX: Artificial Intelligence: [Reference Solutions]

Machine Learning

Machine learning.

📚 Textbooks

Mathematics for Machine Learning

Concise Machine Learning

The Elements of Statistical Learning

Mining of Massive Datasets

Pattern Recognition and Machine Learning: [Codes]

Cross-Industry Process for Data Mining methodology

🏫 Courses

Columbia COMS W4995: Applied Machine Learning 📺 ⭐

Stanford CS229: Machine Learning 📺

Harvard CS 109A Data Science

edX ColumbiaX: Machine Learning

Berkeley CS294: Fairness in Machine Learning

Google: Machine Learning Crash Course

Google: AI Education

Google: Applied Machine Learning Intensive

Cornell Tech CS5785: Applied Machine Learning 📺

Probabilistic Machine Learning (Summer 2020) 📺

AutoML - Automated Machine Learning

Machine Learning Engineering

These courses helps you bridge the gap from training machine learning models to deploy AI systems in the real world.

📚 Textbooks

Machine Learning Engineering

Machine Learning System Design

Microsoft Commercial Software Engineering ML Fundamentals

Google Rules of ML

The Twelve Factors App

Feature Engineering and Selection: A Practical Approach for Predictive Models

Continuous Delivery for Machine Learning

🏫 Courses

Berkeley: Full Stack Deep Learning ⭐

Stanford: CS 329S: Machine Learning Systems Design ⭐

CMU: Machine Learning in Production github

Andrew Ng: Bridging AI's Proof-of-Concept to Production Gap

Facebook Field Guide to Machine Learning

Udemy: Deployment of Machine Learning Models ⭐

Spark

Udemy: The Complete Hands On Course To Master Apache Airflow

Deep Learning Overview

Basic overview for deep learning.

📚 Textbooks

Deep Learning

Dive into Deep Learning

The Matrix Calculus You Need For Deep Learning

🏫 Courses

Berkeley CS 182: Designing, Visualizing and Understanding Deep Neural Networks

Stanford CS 25: Transformers 📺

Deeplearning.ai Deep Learning Specialization: [Reference Solutions] ⭐

NYU: Deep Learning

Specializations

Recommendation Systems

Recommendation system is used when users do not know what they want and cannot use keywords to describe needs.

📚 Textbooks

Mining of Massive Datasets

Speech and Language Processing

Dive into Deep Learning: Chapter 16 Recommender Systems

🏫 Courses

Stanford CS246: Mining Massive Data Sets

Information Retrieval and Web Search

Search and Ranking is used when users have specific needs and can use keywords to describe their needs.

📚 Textbooks

Introduction to Information Retrieval

🏫 Courses

Stanford CS224U: Natural Language Understanding - NLU and Information Retrieval

TU Wein: Crash Course IR - Fundamentals

UIUC: Text Retrieval and Search Engines

Stanford CS276: Information Retrieval and Web Search

University of Freiburg: Information Retrieval 📺

Natural Language Processing

With languages models and sequential models, everyone can write like GPT-3.

📚 Textbook

Deep Learning

Introduction to Natural Language Processing

Speech and Language Processing

🏫 Courses

Stanford CS224n: Natural Language Processing with Deep Learning: [Reference Solutions] ⭐

Berkeley CS182: Designing, Visualizing, and Understanding Deep Neural Networks: 📺 [Reference Solutions]

NYU: DS-GA 1011 Natural Language Processing with Representation Learnin

Deeplearning.ai Natural Language Processing Specialization [Reference Solutions]

Vision

Neural nets cannot solve all vision problems, yet.

📚 Textbooks

Deep Learning

🏫 Courses

Stanford CS231n: Convolutional Neural Networks for Visual Recognition: [Assignment 2 Solution, Assignment 3 Solution] ⭐

Berkeley CS182: Designing, Visualizing, and Understanding Deep Neural Networks: 📺 [Reference Solutions]

Unsupervised Learning and Generative Models

🏫 Courses

Stanford CS236: Deep Generative Models

Berkeley CS294-158: Deep Unsupervised Learning

Foundation Models

Stanford CS234: Large Language Models (Winter 2022)

Stanford CS234: Advances in Foundation Models (Winter 2023)

Reinforcement Learning

📚 Textbook

Reinforcement Learning

Deep Learning

🏫 Courses

Coursera: Reinforcement Learning Specialization <= Recommended by Richard Sutton, the author of the de facto textbook on RL. ⭐

Berkeley CS182: Designing, Visualizing, and Understanding Deep Neural Networks: 📺 [Reference Solutions]

Stanford CS234: Reinforcement Learning

Berkeley CS285: Deep Reinforcement Learning ⭐

CS 330: Deep Multi-Task and Meta Learning: Videos

Berekley: Deep Reinforcement Learning Bootcamp

OpenAI Spinning Up

IDS at Stanford RL forum Video 1 Video 2 Slides

Robotics 🤖

Quaternions, quaternions everywhere. And gradients.

🏫 Courses

ColumbiaX: CSMM.103x Robotics

CS 287: Advanced Robotics

LICENSE

All books, blogs, and courses are owned by their respective authors.

You can use my compilation and my reference solutions under the open CC BY-SA 3.0 license and cite it as:

@misc{leehanchung,
  author = {Lee, Hanchung},
  title = {Full Stack Machine Learning Engineering Courses},
  year = {2020},
  howpublished = {Github Repo},
  url = {https://github.com/awesome-full-stack-machine-learning-courses}
}