A visual course on the fundamentals of LLMs including text representation, text generation, semantic search and prompt engineering.

A practical course for building LLM-based applications with Charles Frye, Sergey Karayev, and Josh Tobin.

A technical primer on vector databases. This course covers vector database core concepts, understanding search similarity, indexing and querying, and more.

Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. This course is divided into three parts: 1. LLM Fundamentals covers essential knowledge about mathematics, Python, and neural networks. 2. The LLM Scientist focuses on building the best possible LLMs using the latest techniques. 3. The LLM Engineer focuses on creating LLM-based applications and deploying them. For an interactive version of this course, Maxime has created two LLM assistants that will answer questions and test your knowledge in a personalized way.

Design, train, and deploy models from A to Z. This is also a great resource for those struggling with the machine learning system design questions in interviews.

Probabilistic Graphical Models. Master a new way of reasoning and learning in complex domains.

One of the best linear algebra courses taught by the legendary professor Gilbert Strang. This is a basic subject on matrix theory and linear algebra. Emphasis is given to topics that will be useful in other disciplines, including systems of equations, vector spaces, determinants, eigenvalues, similarity, and positive definite matrices.

Self-paced course covers basic concepts in probability and statistics spanning over four fundamental aspects of machine learning: exploratory data analysis, producing data, probability, and inference.

Bite-sized education to boost your AI knowledge - from beginner to pro. Learn through courses, workshops and their blog posts.

Suggested reading from Ilya Sutskever, OpenAI’s chief scientist, for John Carmack.

An educational resource produced by OpenAI that makes it easier to learn about deep reinforcement learning (deep RL).

A 12-week, 24-lesson curriculum for Artificial Intelligence from Microsoft. It includes practical lessons, quizzes, and labs. The curriculum is beginner-friendly and covers tools like TensorFlow and PyTorch, as well as ethics in AI.

This modern and comprehensive Data Science and Machine Learning course on Udemy teaches you the latest skills using Python, TensorFlow 2.0, and other libraries. It covers everything from basic Python programming to advanced topics like Neural Networks, Deep Learning, and Transfer Learning. You'll build real-world projects, gain hands-on experience, and learn to work with popular tools such as Pandas, Scikit-learn, and Matplotlib. By the end, you'll have a portfolio of projects and the skills needed to become a professional Data Scientist, ready for the job market.

This MIT course introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence. Upon completion, students should be able to develop intelligent systems by assembling solutions to concrete computational problems; understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering; and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective.

A free course from fast.ai designed for people with some coding experience, who want to learn how to apply deep learning and machine learning to practical problems.

A non-technical course on AI technologies taught by Andrew Ng, one of the industry leaders in Artificial Intelligence. This course covers what AI is, building AI projects, AI in your company and AI and society.

A course focused on specializing in Deep Learning taught by Andrew Ng, one of the industry leaders in Artificial Intelligence. This course covers neural networks and deep learning, improving neural networks, structuring machine learning projects, convolutional neural networks and sequence models.

A course on implementing Machine Learning in production taught by Andrew Ng, one of the industry leaders in Artificial Intelligence. This course covers the ML lifecycle and deployment, modelling challenges and strategies and data definition and baseline.

A course on specializing in Machine Learning taught by Andrew Ng, one of the industry leaders in Artificial Intelligence. This course covers supervised machine learning, advanced learning algorithms, unsupervised learning and recommender systems.