:books: Textbooks

• Artificial Intelligence: A Modern Approach
• Berkeley CS188: Artificial Intelligence

• edX ColumbiaX: Artificial Intelligence - Core AI concepts and algorithms with programming projects. [[Reference Solutions](https://github.com/leehanchung/CSMM-101x-AI)]

Robotics

• NerdWallet: How NerdWallet Dialed Machine Learning Up to 11 - Case study on scaling ML infrastructure at fintech company.
• AI Dungeon: How we scaled AI Dungeon 2 to support over 1,000,000 users - Infrastructure lessons from scaling a generative AI application.
• Trigo: How Trigo built a scalable AI development & deployment pipeline for Frictionless Retail - AI/ML pipeline development for computer vision retail applications.
• ByteDance: How TikTok Wins The Social Media Recommendation System War - Technical breakdown of TikTok's recommendation algorithm. (transcription)
• NerdWallet: How NerdWallet Dialed Machine Learning Up to 11 - Case study on scaling ML infrastructure at fintech company.
• Spotify: The Winding Road to Better Machine Learning Infrastructure Through Tensorflow Extended and Kubeflow - Building ML infrastructure for music recommendation at Spotify.

:books: Textbooks

• Grokking Algorithms
• Google Python Style Guide
• Python3 Patterns
• Design Patterns: Elements of Reusable Object-Oriented Software 1st Edition
• Python Design Patterns

:school: Courses

• MIT: The Missing Sememster of Your CS Education
• 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 - WkMbsvGQk9_UB)
• SQL for Data Analysis
• MIT 6.824: Distributed Systems - WkMbsvGQk9_UB)

• edX MITX: Introduction to Computer Science and Programming Using Python - Learn Python fundamentals through problem-solving and applications. :star:

:books: Textbooks

• Dive into Deep Learning
• The Matrix Calculus You Need For Deep Learning
• Stanford CS 25: Transformers - 8-Y&list=PLoROMvodv4rNiJRchCzutFw5ItR_Z27CM)
• Deeplearning.ai Deep Learning Specialization
• NYU: Deep Learning
• The Matrix Calculus You Need For Deep Learning
• NYU: Deep Learning

:books: Textbooks

• Mathematics for Machine Learning
• Concise Machine Learning
• The Elements of Statistical Learning
• Mining of Massive Datasets
• Pattern Recognition and 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
• Probabilistic Machine Learning (Summer 2020)
• AutoML - Automated Machine Learning
• MIT: Data Centric AI
• Google: Applied Machine Learning Intensive
• Cornell Tech CS5785: Applied Machine Learning

• edX ColumbiaX: Machine Learning - Core machine learning algorithms and applications.

:books: 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
• Stanford: CS 329S: Machine Learning Systems Design
• CMU: Machine Learning in Production
• Andrew Ng: Bridging AI's Proof-of-Concept to Production Gap
• Facebook Field Guide to Machine Learning
• Udemy: Deployment of Machine Learning Models
• Udemy: The Complete Hands On Course To Master Apache Airflow
• Feature Engineering and Selection: A Practical Approach for Predictive Models

• Spark - Large-scale data processing with Apache Spark.
• Facebook Field Guide to Machine Learning - Machine learning practices from Facebook.

:books: Textbooks

• NIST Engineering Statistics Handbook
• Stanford Stats216: Statiscal Learning
• CalTech: Learning From Data
• A Students Guide to Bayesian Statistics
• Introduction to Linear Algebra for Applied Machine Learning with Python

• A Students Guide to Bayesian Statistics - Introduction to Bayesian methods and probabilistic thinking.

Foundation Models

• Stanford CS234: Large Language Models (Winter 2022)
• Stanford CS234: Advances in Foundation Models (Winter 2023)

Information Retrieval and Web Search

• Introduction to Information Retrieval
• 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
• UIUC: Text Retrieval and Search Engines
• Stanford CS276: Information Retrieval and Web Search
• Stanford CS224U: Natural Language Understanding - NLU and Information Retrieval - NLU methods for information retrieval and question answering.
• TU Wein: Crash Course IR - Fundamentals - Fundamentals of information retrieval systems.

Natural Language Processing

• Speech and Language Processing
• Introduction to Natural Language Processing
• Stanford CS224n: Natural Language Processing with Deep Learning
• NYU: DS-GA 1011 Natural Language Processing with Representation Learnin
• Deeplearning.ai Natural Language Processing Specialization - nlp-specialization)]
• NYU: DS-GA 1011 Natural Language Processing with Representation Learnin
• NYU: DS-GA 1011 Natural Language Processing with Representation Learning - NLP with representation learning and neural methods.

Recommendation Systems

• Dive into Deep Learning: Chapter 16 Recommender Systems
• Stanford CS246: Mining Massive Data Sets
• Dive into Deep Learning: Chapter 16 Recommender Systems
• Stanford CS246: Mining Massive Data Sets

Reinforcement Learning

• Reinforcement Learning
• Coursera: Reinforcement Learning Specialization
• Stanford CS234: Reinforcement Learning
• Berkeley CS285: Deep Reinforcement Learning
• Berekley: Deep Reinforcement Learning Bootcamp
• OpenAI Spinning Up
• Video 1 - FCNryj-GUn2a.21yA0Q1WPwhwZMgF?startTime=1610560965000) [Slides](https://drive.google.com/file/d/1KSFVptieJ-b115mLqAYfp2pVhJZ02qWh/view?usp=sharing)
• Reinforcement Learning

Robotics

• ColumbiaX: CSMM.103x Robotics - Robotics fundamentals covering kinematics, dynamics, and control.

Robotics :robot:

• ColumbiaX: CSMM.103x Robotics
• CS 287: Advanced Robotics

Unsupervised Learning and Generative Models

• Stanford CS236: Deep Generative Models
• Berkeley CS294-158: Deep Unsupervised Learning