Self Study Guide for Full Stack Machine Learning Engineering
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.
Computer Science
📺 Course
edX MITX: Introduction to Computer Science and Programming Using Python
Machine Learning
📖 Textbook
The Elements of Statistical Learning
📺 Course
MIT 18.05: Introduction to Probability and Statistics
Stanford Stats216: Statiscal Learning
edX ColumbiaX: Machine Learning
Machine Learning Project Design, Pipeline, and Deployment
📖 Textbook
Machine Learning: The High Interest Credit Card of Technical Debt
📺 Course
Berkeley: Full Stack Deep Learning
Udemy: Deployment of Machine Learning Models
Udemy: The Complete Hands On Course To Master Apache Airflow
Artificial Intelligence
📖 Textbook
Artificial Intelligence: A Modern Approach
📺 Course
Berkeley CS188: Artificial Intelligence
edX ColumbiaX: Artificial Intelligence
Specializations
Vision
📖 Textbook
📺 Course
Stanford CS231n: Convolutional Neural Networks for Visual Recognition
Berkeley CS182: Designing, Visualizing, and Understanding Deep Neural Networks
Natural Language Programming
📖 Textbook
📺 Course
Stanford CS224n: Natural Language Processing with Deep Learning
Berkeley CS182: Designing, Visualizing, and Understanding Deep Neural Networks
Deep Reinforcement Learning
📖 Textbook
📺 Course
Berkeley CS182: Designing, Visualizing, and Understanding Deep Neural Networks
Berekley: Deep Reinforcement Learning Bootcamp
Berkeley CS285: Deep Reinforcement Learning
Unsupervised Learning and Generative Models
📺 Course
Stanford CS236: Deep Generative Models