Learn the foundations of production-ready ML, including system architecture, productionization, and performance tuning. Machine learning is driving exciting changes and progress in computing. What does the ubiquity of machine learning mean for how pe…
AI Trainings
Access various online training courses for free and get upskilled in your own time.
Stanford CS236 – Generative AI
Build intuition on modern gen-AI, including diffusion models, VAEs & flows, and image synthesis. Generative models are widely used in many subfields of AI and Machine Learning. Recent advances in parameterizing these models using deep neural networks…
Stanford CS336 – Language Models
Deeply understand data preproc, LLM building blocks, evals, scaling, and reasoning. Language models serve as the cornerstone of modern natural language processing (NLP) applications and open up a new paradigm of having a single general purpose system…
CS224N: NLP with Deep Learning
Natural language processing (NLP) is a crucial part of artificial intelligence (AI), modeling how people share information. In recent years, deep learning approaches have obtained very high performance on many NLP tasks.
CS224U: Natural Language Understanding
This project-oriented class is focused on developing systems and algorithms for robust machine understanding of human language. It draws on theoretical concepts from linguistics, natural language processing, and machine learning.
CS234: Reinforcement Learning
To realize the dreams and impact of AI requires autonomous systems that learn to make good decisions. Reinforcement learning is one powerful paradigm for doing so, and it is relevant to an enormous range of tasks, including robotics, game playing, co…
CS229M: Machine Learning Theory
When do machine learning algorithms work and why? How do we formalize what it means for an algorithm to learn from data? How do we use mathematical thinking to design better machine learning methods? This course focuses on developing a theoretical un…
CS230: Deep Learning (Andrew Ng)
Deep Learning is one of the most highly sought after skills in AI. In this course, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn ab…
CS229: Machine Learning (Andrew Ng)
This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative learning, parametric/non-parametric learning, neural networks); unsupervised learning (clustering, dim…
CS221: Artificial Intelligence
The goal of artificial intelligence (AI) is to tackle complex real-world problems with rigorous mathematical tools. In this course, you will learn the foundational principles and practice implementing various AI systems.
Stanford MLSys
Learn the foundations of production-ready ML, including system architecture, productionization, and performance tuning. Machine learning is driving exciting changes and progress in computing. What does the ubiquity of machine learning mean for how people build and deplo…
Stanford CS236 – Generative AI
Build intuition on modern gen-AI, including diffusion models, VAEs & flows, and image synthesis. Generative models are widely used in many subfields of AI and Machine Learning. Recent advances in parameterizing these models using deep neural networks, combined with prog…
Stanford CS336 – Language Models
Deeply understand data preproc, LLM building blocks, evals, scaling, and reasoning. Language models serve as the cornerstone of modern natural language processing (NLP) applications and open up a new paradigm of having a single general purpose system address a range of …
CS224N: NLP with Deep Learning
Natural language processing (NLP) is a crucial part of artificial intelligence (AI), modeling how people share information. In recent years, deep learning approaches have obtained very high performance on many NLP tasks.
CS224U: Natural Language Understanding
This project-oriented class is focused on developing systems and algorithms for robust machine understanding of human language. It draws on theoretical concepts from linguistics, natural language processing, and machine learning.
CS234: Reinforcement Learning
To realize the dreams and impact of AI requires autonomous systems that learn to make good decisions. Reinforcement learning is one powerful paradigm for doing so, and it is relevant to an enormous range of tasks, including robotics, game playing, consumer modeling and …
CS229M: Machine Learning Theory
When do machine learning algorithms work and why? How do we formalize what it means for an algorithm to learn from data? How do we use mathematical thinking to design better machine learning methods? This course focuses on developing a theoretical understanding of the s…
CS230: Deep Learning (Andrew Ng)
Deep Learning is one of the most highly sought after skills in AI. In this course, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional ne…
CS229: Machine Learning (Andrew Ng)
This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative learning, parametric/non-parametric learning, neural networks); unsupervised learning (clustering, dimensionality reductio…
CS221: Artificial Intelligence
The goal of artificial intelligence (AI) is to tackle complex real-world problems with rigorous mathematical tools. In this course, you will learn the foundational principles and practice implementing various AI systems.