This is a self study guide for learning full stack machine learning engineering, break down by topics and specializations. Python is the preferred framework as it covers the whole machine learning engineering framework from end-to-end.
edX MITX: Introduction to Computer Science and Programming Using Python
edX Harvard: CS50x: Introduction to Computer Science
The Elements of Statistical Learning
MIT 18.05: Introduction to Probability and Statistics
Stanford Stats216: Statiscal Learning
edX ColumbiaX: Machine Learning
Machine Learning: The High Interest Credit Card of Technical Debt
Berkeley: Full Stack Deep Learning
Udemy: Deployment of Machine Learning Models
Udemy: The Complete Hands On Course To Master Apache Airflow
Artificial Intelligence: A Modern Approach
Berkeley CS188: Artificial Intelligence
edX ColumbiaX: Artificial Intelligence: [Reference Solutions]
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]
Stanford CS224n: Natural Language Processing with Deep Learning: [Reference Solutions]
Berkeley CS182: Designing, Visualizing, and Understanding Deep Neural Networks: [Reference Solutions]
Berkeley CS182: Designing, Visualizing, and Understanding Deep Neural Networks: [Reference Solutions]
Berkeley CS285: Deep Reinforcement Learning
Berekley: Deep Reinforcement Learning Bootcamp
Stanford CS236: Deep Generative Models