Shortest Path to LLM / Agents

Bare minimum list of courses to go through for basic background knowledge in LLM and AI Agents.

• Berkeley CS188: Artificial Intelligence - Foundational course covering search, planning, and reasoning essential for understanding AI agents. :star:

• Stanford CS231n: Convolutional Neural Networks for Visual Recognition - Deep learning for computer vision with practical assignments. [Assignment 2 Solution, Assignment 3 Solution] :star:

• Stanford CS224n: Natural Language Processing with Deep Learning - Covers neural networks for NLP, language models, and transformers. [Reference Solutions] :star:

• Berkeley CS285: Deep Reinforcement Learning - Comprehensive course on deep RL algorithms and policy gradient methods. :star:

• Stanford CS336: Language Modeling from Scratch - Modern approach to building language models from first principles. :star:

TL;DR

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

• MIT: The Missing Semester of Your CS Education - Essential practical skills for computer science and software development.

• edX Harvard: CS50x: Introduction to Computer Science - Comprehensive introduction to computer science fundamentals.

• MIT 18.05: Introduction to Probability and Statistics - Foundation for statistical understanding in machine learning.

• Columbia COMS W4995: Applied Machine Learning - Applied ML with practical projects and real-world examples. :tv:

• MIT 18.06: Linear Algebra - Essential mathematical foundation for machine learning.

• Berkeley CS182: Designing, Visualizing, and Understanding Deep Neural Networks - Deep learning fundamentals with visual explanations and code. :tv: [Reference Solutions]

• Berkeley: Full Stack Deep Learning - End-to-end ML engineering covering infrastructure and deployment.

Computer Science

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

:books: Textbooks

• Grokking Algorithms - Visual introduction to algorithms and data structures with clear illustrations.

• Google Python Style Guide - Industry standard Python code style and conventions.

• Python Design Patterns - Common Python patterns and idioms for writing clean code.

• Python3 Patterns - Python 3 specific patterns and best practices.

• Design Patterns: Elements of Reusable Object-Oriented Software 1st Edition - Foundational book on software design patterns.

:school: Courses

• MIT: The Missing Semester of Your CS Education - Essential practical tools and skills for software development. :star:

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

• edX Harvard: CS50x: Introduction to Computer Science - Comprehensive CS fundamentals from Harvard University.

• SQL for Data Analysis - Learn SQL for querying and analyzing data.

• PostgreSQL Exercises - Hands-on SQL practice with real-world scenarios.

• U Waterloo: CS794: Optimization for Data Science - Optimization techniques essential for machine learning.

• Berkeley CS 170: Efficient Algorithms and Intractable Problems - Study of algorithms, computational complexity, and NP-completeness.

• Berkeley CS 294-165: Sketching Algorithms - Algorithms for processing massive datasets efficiently.

• MIT 6.824: Distributed Systems - Foundations of distributed systems and fault tolerance. :tv:

Math and Statistics

Linear algebra, statistics, and mathematical foundations for machine learning.

:books: Textbooks

• NIST Engineering Statistics Handbook - Comprehensive reference on statistical methods and applications.

:school: Courses

• MIT 18.05: Introduction to Probability and Statistics - Essential probability and statistics for understanding machine learning. :star:

• MIT 18.06: Linear Algebra - Comprehensive linear algebra covering vectors, matrices, and eigenvalues. :star:

• Stanford Stats216: Statistical Learning - Statistical methods for learning from data with R labs. :star:

• CalTech: Learning From Data - Theoretical foundations of machine learning and generalization.

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

• Introduction to Linear Algebra for Applied Machine Learning with Python - Practical linear algebra with Python applications.

Artificial Intelligence

Artificial Intelligence is the superset of Machine Learning. These courses provide a high-level understanding of the field of AI, including searching, planning, logic, constraint optimization, and machine learning.

:books: Textbooks

• Artificial Intelligence: A Modern Approach - Comprehensive textbook on AI algorithms and techniques.

:school: Courses

• Berkeley CS188: Artificial Intelligence - Foundational AI course covering search, planning, reasoning, and learning. :star:

• edX ColumbiaX: Artificial Intelligence - Core AI concepts and algorithms with programming projects. [Reference Solutions]

Machine Learning

Core machine learning theory and applied methods.

:books: Textbooks

• Mathematics for Machine Learning - Essential mathematics for understanding machine learning algorithms.

• Concise Machine Learning - Concise overview of key machine learning concepts.

• The Elements of Statistical Learning - Statistical foundations of supervised learning.

• Mining of Massive Datasets - Algorithms for processing and analyzing large-scale data.

• Pattern Recognition and Machine Learning - Comprehensive coverage of pattern recognition techniques. [Codes]

• Cross-Industry Process for Data Mining methodology - Standard process for data mining and analytics projects.

:school: Courses

• Columbia COMS W4995: Applied Machine Learning - Applied ML with hands-on projects and real-world problem solving. :tv: :star:

• Stanford CS229: Machine Learning - Comprehensive ML course covering supervised, unsupervised, and reinforcement learning. :tv:

• Harvard CS 109A Data Science - Data science fundamentals and machine learning methods.

• [edX Columb