Awsome AI & ML Resources: Online ML University
Many students/AI enthusiasts have questions about where to start with Machine Learning. There are learning paths out there that suggest what to learn, they often miss the main question - 'where do I learn?' Luckily, there are tons of free courses available from top universities like Stanford, Harvard, MIT, and CMU - covering basic to advanced topics. Now, the best part is that these courses not only provide lectures but also class slides, codes, and detailed lecture plans. To make things even easier, I've compiled a list of these courses in thus repository. You'll find all links of different courses from top universities. It's all free and accessible to anyone.
This repository contains a curated list of top AI courses offered by renowned universities. Each course is handpicked to ensure that it covers the latest topics and technologies in the field of AI.
- Cheat Sheets Collection
- Artificial Intelligence, Machine Learning
- Computer Science
- Statistics
- Data Science
- Deep Learning
- Computer Vision
- Natural Language Processing
- Generative AI & LLMs
- Computational Biology and Bioinformatics
- Generative AI with Vision
- Graph Neural Networks
- Reinforcement Learning
- Time Series/Audio/Signal Processing
- MLOps
- Psychology and Cognitive Modeling
- Unsupervised Learning
- Explainable AI
- AI for Optimization
- Computational Neuroscience + ML
- Trustworthiness and fairness in ML
- Robotics and Autonomous Systems
- Computer Human Interaction (CHI) or Human Computer Interaction (HCI)
| Source | Course Code | Course Name | Session | Difficulty | URL |
|---|---|---|---|---|---|
| Stanford University | Stanford CS229 | Machine Learning | Spring 2022 | ⭐⭐ | Youtube |
| Stanford University | Stanford CS229 | Machine Learning Full Course taught by Andrew Ng | Autumn 2018 | ⭐⭐ | Youtube |
| Stanford University | Stanford CS221 | Artificial Intelligence: Principles and Techniques | Autumn 2021 | ⭐⭐ | Youtube |
| Stanford University | Stanford CS229M | Machine Learning Theory | Fall 2021 | ⭐⭐⭐ | Youtube |
| Stanford University | Stanford CS229 | Machine Learning Course | Summer 2019 | ⭐⭐ | Youtube |
| Stanford University | Stanford EE104 | Introduction to Machine Learning Full Course | N/A | ⭐⭐ | Youtube |
| MIT | 6.034 | Artificial Intelligence | Fall 2010 | ⭐⭐⭐ | Youtube |
| UC Berkeley | CS 188 | Introduction to Artificial Intelligence | Fal 2018 | ⭐ | Youtube |
| Carnegie Mellon University | CS/LTI 11-777 | Multimodal Machine Learning | ⭐⭐⭐ | Youtube | |
| Machine Learning Crash Course | URL | ||||
| Harvard | CS197 | AI Research Experiences | - | ⭐⭐⭐ | Course Website |
| The State of Competitive Machine Learning | - | ⭐⭐⭐ | Website | ||
| National University of Singapore | Uncertainty Modeling in AI | - | ⭐⭐ | Youtube | |
| Basics of Machine Learning | ⭐ | URL | |||
| Kaggle | Intro to AI Ethics | ⭐ | URL | ||
| Class Central | Elements of AI | ⭐ | URL | ||
| Udacity | Intro to TensorFlow for Deep Learning | ⭐⭐ | URL | ||
| NYU | CSCI-UA.0473-001 | Introduction to Machine Learning | - | ⭐ | Website |
| - | - | Machine Learning Bookcamp by Alexey Grigorev | - | ⭐ | GitHub |
| University of Tübingen | - | Probabilistic ML by Prof. Dr. Philipp Hennig | 2023 | ⭐⭐ | Youtube |
| University of Tübingen | - | Statistical Machine Learning — Ulrike von Luxburg | 2020 | ⭐⭐ | Youtube |
| University of Tübingen | - | Mathematics for Machine Learning — Ulrike von Luxburg | 2020 | ⭐⭐ | Youtube |
| University of Tübingen | - | Neural Data Science — Philipp Berens | 2021 | ⭐⭐ | Youtube |
| University of Tübingen | - | Introduction to Machine Learning — Dmitry Kobak | 2020/21 | ⭐⭐ | Youtube |
| University of Tübingen | - | Data Compression With Deep Probabilistic Models | ⭐⭐ | Youtube |
| Source | Course Code | Course Name | Session | Difficulty | URL |
|---|---|---|---|---|---|
| Stanford University | CS105 | Introduction to Computers Full Course | N/A | ⭐ | Youtube |
| MIT | 6.0001 | Introduction to Computer Science and Programming in Python | Fall 2016 | ⭐ | Youtube |
| MIT | 6.0002 | Introduction to Computational Thinking and Data Science | Fall 2016 | ⭐ | Youtube |
| MIT | 6.006 | Introduction to Algorithms | Spring 2020 | ⭐⭐ | Youtube |
| MIT | 6.042J | Mathematics for Computer Science | Spring 2015 | ⭐⭐ | Youtube |
| Harvard | Introduction to Computer Science | 2015 | ⭐ | Youtube | |
| Princeton University | Algorithms, Part I | ⭐ | Coursera - Free Audit | ||
| Princeton University | Algorithms, Part II | ⭐⭐ | Coursera - Free Audit | ||
| Microsoft and Linkedin Learning | Career Essentials in Software Development | ⭐ | Link (Free) | ||
| Harvard | CS50's Introduction to Programming with Scratch | ⭐ | Course Website |
| Source | Course Code | Course Name | Session | Difficulty | URL |
|---|---|---|---|---|---|
| UCL x DeepMind | Deep Learning Course | 2018 | ⭐⭐⭐ | Youtube | |
| UCL x DeepMind | Deep Learning Lecture Series | 2020 | ⭐⭐⭐ | Youtube | |
| UCL x DeepMind | Deep Learning Lecture Series | 2021 | ⭐⭐⭐ | Youtube | |
| New York University | Deep Learning by Yann LeCun | Spring, 2021 | ⭐⭐ | Youtube | |
| UC Berkeley | STAT-157 | Deep Learning | 2019 | ⭐⭐ | Youtube |
| UC Berkeley | CS 182 | Deep Learning | Spring 2021 | ⭐⭐ | Youtube |
| Carnegie Mellon University | CS/LTI 11-785 | Introduction to Deep Learning | ⭐ | Youtube | |
| Kaggle | Intro to Deep Learning | ⭐ | URL | ||
| Fast.ai | Practical Deep Learning for Coders | ⭐ | URL | ||
| Lightning.AI | Deep Learning Fundamentals | ⭐ | URL | ||
| UC Berkley/ The Full Stack | Reproducible Deep Learning by Simone Scardapane | ⭐⭐ | URL | ||
| The Full Stack | Full Stack Deep Learning - 2022 Course | 2022 | ⭐⭐ | URL | |
| Stanford | CS324W | Foundation Models and their Applications | Winter 2023 | ⭐⭐ | Website |
| Calmcode | - | Embedding Course (Highly Recommended!!) | Winter 2023 | ⭐ | Website |
| University of Tübingen | - | Neural Data Science — Philipp Berens | 2021 | ⭐⭐ | Youtube |
| University of Tübingen | - | Deep Learning — Andreas Geiger | 2022 | ⭐⭐ | Youtube |
| University of Tübingen | - | Math for Deep Learning — Andreas Geiger | 2020 | ⭐⭐ | Youtube |
| Source | Course Name | Difficulty | URL |
|---|---|---|---|
| Microsoft and Linkedin Learning | Career Essentials in Generative AI by Microsoft and LinkedIn | ⭐ | Link (Free) |
| Generative AI learning path | ⭐ | Link (Free) |
| Source | Course Name | Difficulty | URL |
|---|---|---|---|
| DeepLearning.AI | How Diffusion Models Work | ⭐ | Link (Free) |
| Hugging Face | Diffusion Models Course | ⭐⭐ | Youtube |
| Fast.ai | From Deep Learning Foundations to Stable Diffusion | ⭐⭐ | Website |
| Source | Course Code | Course Name | Session | Difficulty | URL |
|---|---|---|---|---|---|
| Stanford University | Stanford CS224W | Machine Learning with Graphs | N/A | ⭐⭐⭐ | Youtube |
| DeepFindr | - | Graph Neural Networks | N/A | ⭐ | Youtube |
| WelcomeAIOverlords | - | Graph Neural Networks | N/A | ⭐ | Youtube |
| ML Explained - Aggregate Intellect - AI.SCIENCE | - | Graph Neural Networks (Hands On) | N/A | ⭐⭐ | Youtube |
| Aleksa Gordić - The AI Epiphany | - | Graph Neural Networks | N/A | ⭐⭐ | Youtube |
| African Master in Machine Intelligence | Geometric Deep Learning Oxford-NYU-Qualcomm-DeepMind | 2022 | ⭐⭐ | Youtube |
| Source | Course Code | Course Name | Session | Difficulty | URL |
|---|---|---|---|---|---|
| University of Michigan | Data Science Ethics | ⭐ | Coursera (Free) | ||
| Harvard | Data Science: Machine Learning | - | ⭐ | Course Website | |
| Stanford University | Stanford CS472 | Data science and AI for COVID-19 | N/A | ⭐ | Youtube |
| University of London | Data Science | Foundations of Data Science: K-Means Clustering in Python | ⭐ | Coursera - Free Audit | |
| Microsoft and Linkedin Learning | Career Essentials in Data Analysis by Microsoft and LinkedIn | ⭐ | Link (Free) | ||
| Microsoft and Linkedin Learning | Career Essentials in Business Analysis by Microsoft and LinkedIn | ⭐ | Link (Free) | ||
| Data Science with Python | URL | ||||
| Harvard | High-Dimensional Data Analysis | - | ⭐ | Course Website | |
| University of Tübingen | - | Neural Data Science — Philipp Berens | 2021 | ⭐⭐ | Youtube |
| Source | Course Code | Course Name | Session | Difficulty | URL |
|---|---|---|---|---|---|
| Stanford | Stanford CS224N | Natural Language Processing with Deep Learning | Winter 2021 | ⭐⭐⭐ | Youtube |
| Stanford | Stanford XCS224U | Natural Language Understanding | Spring 2023 | ⭐⭐⭐ | Youtube |
| Stanford | Stanford CS224U | Natural Language Understanding | Spring 2021 | ⭐⭐⭐ | Youtube |
| Stanford | Stanford CS25 | Transformers United | N/A | ⭐⭐ | Youtube |
| Carnegie Mellon University | CS/LTI 11-711 | Advanced NLP | ⭐⭐⭐ | Youtube | |
| Carnegie Mellon University | CS/LTI 11-747 | Neural Networks for NLP | ⭐⭐ | Youtube | |
| Carnegie Mellon University | CS/LTI 11-737 | Multilingual NLP | ⭐⭐⭐ | Youtube | |
| Carnegie Mellon University | CS/LTI Bootcamp | Low Resource NLP Bootcamp 2020 by Graham Neubig | ⭐⭐⭐ | Youtube | |
| Hugging Face | NLP | ⭐ | Link (Free) | ||
| NYU | LING-UA 52, DS-UA 203 | Machine Learning for Language Understanding (Sam Bowman) | Spring 2022 | ⭐⭐⭐ | Website - Google Docs |
| NYU | DS-GA 1012 | Natural Language Understanding and Computational Semantics (Sam Bowman) | Spring 2022 | ⭐⭐⭐ | Website - Google Docs |
| NYU | CS-GA 3033 | Mathematics of Deep Learning (Joan Bruna) | Spring 2022 | ⭐⭐⭐ | Website-Notion |
| NYU | DS-GA 1011 | Natural Language Processing with Representation Learning | Fall 2020 | ⭐⭐⭐ | Website - Google Docs |
| NYU | LING-GA 3340 | Seminar in Semantics | - | ⭐⭐⭐ | Website |
| Source | Course Code | Course Name | Session | Difficulty | URL |
|---|---|---|---|---|---|
| Stanford | N/A | Convolutional Neural Networks for Visual Recognition | N/A | ⭐⭐ | Youtube |
| MIT | 6.801 | Machine Vision | Fall 2020 | ⭐⭐ | Youtube |
| MIT | 6.S192 | Deep Learning for Art, Aesthetics, and Creativity by Ali Jahanian | N/A | ⭐⭐ | Youtube |
| Carnegie Mellon University | 16-385 | Computer Vision | Spring 2022 | ⭐⭐⭐ | Website |
| University of Michigan | - | Deep Learning for Computer Vision | ⭐⭐ | Youtube | |
| - | - | An Invitation to 3D Vision: A Tutorial for Everyone | - | ⭐⭐ | Github |
| UC Berkeley | NIPS 2016 | Deep Learning for Action and Interaction Workshop | 2016 | ⭐⭐⭐ | Youtube |
| UC Berkeley | CS 198-126 | Modern Computer Vision | Fal 2022 | ⭐⭐⭐ | Youtube |
| UC Berkeley | CS194-26/294-26 | Intro to Computer Vision and Computational Photography | ⭐⭐ | Website | |
| Roboflow | Computer Vision in Practice | ⭐⭐ | Youtube | ||
| National University Singapore | 3D Computer Vision | ⭐⭐ | Youtube | ||
| Columbia University in New York | 3D Reconstruction - Single Viewpoint | ⭐⭐ | Coursera (Audit) | ||
| Stanford | CS231A | Computer Vision, From 3D Reconstruction to Recognition | ⭐⭐ | Website (Slides) | |
| Carnegie Mellon University | 16-889 | Learning for 3D Vision | Spring 2023 | ⭐⭐ | Website |
| Carnegie Mellon University | 15-463, 15-663, 15-862 | Computational photography | Fall 2022 | ⭐⭐⭐ | Website |
| Carnegie Mellon University | 15-468, 15-668, 15-868 | Physics-based rendering | Spring 2023 | ⭐⭐⭐ | Website |
| Carnegie Mellon University | 16-726 | Learning-Based Image Synthesis | Spring 2023 | ⭐⭐⭐ | Website |
| Carnegie Mellon University | 16-822 | Geometry-based Methods in Vision | Fall 2022 | ⭐⭐⭐ | Website |
| Carnegie Mellon University | CSCI 5980 | Multiview 3D Geometry in Computer Vision | Spring 2018 | ⭐⭐⭐ | Website |
| Carnegie Mellon University | CS 598 | 3D Vision | Fall 2021 | ⭐⭐⭐ | Website |
| UNIVERSITY OF ILLINOIS URBANA-CHAMPAIGN | 16-823 | Physics based Methods in Vision | Spring 2020 | ⭐⭐⭐ | Website |
| Carnegie Mellon University | 16-824 | Visual Learning and Recognition | Spring 2023 | ⭐⭐⭐ | Website |
| Cornell Tech | CS5670 | Introduction to Computer Vision | Spring 2022 | ⭐⭐ | Website |
| MIT | 6.819/6.869 | Advances in Computer Vision | Spring 2021 | ⭐⭐⭐ | Website |
| Carnegie Mellon University | 16-721 | Learning-Based Methods in Vision | Spring 2007 | ⭐⭐⭐ | Website |
| CSCI 1430, Spring 2023 | Computer Vision | Spring 2023 | ⭐⭐ | Website | |
| University of Taxus | CS 378 | Computer Vision | Fall 2009 | ⭐⭐ | Website |
| IMPA | - | Fundamentals and Trends in Vision and Image Processing | August-November 2021 | ⭐⭐⭐ | Website |
| Carnegie Mellon University | Learning for 3D Vision | Spring 2023 | ⭐⭐⭐ | Website | |
| University of Michigan | EECS 442 | Computer Vision | Winter 2021 | ⭐⭐ | Website |
| Georgia Tech | CS 4476 | Introduction to Computer Vision | Fall 2019 | ⭐⭐ | Website |
| EPFL | CS-442 | Computer Vision | 2020/2021 | ⭐⭐ | Website |
| New York University | CSCI-GA.2271-001 | Computer Vision | Fall 2022 | ⭐⭐ | Website |
| UCF Center for Research in Computer Vision | CAP6412 | Advanced Computer Vision | Spring 2023 | ⭐⭐ | Youtube |
| University of Tübingen | - | Computer Vision — Andreas Geiger | ⭐⭐ | Youtube |
| Source | Course Code | Course Name | Session | Difficulty | URL |
|---|---|---|---|---|---|
| NYU | CSCI-UA.480-072 | Robot Intelligence (Lerrel Pinto) | Spring 2022 | ⭐⭐⭐ | Website |
| MIT | - | Introduction To Robotics | Fall 2005 | ⭐⭐⭐ | Website |
| University of Tübingen | - | Self-Driving Cars — Andreas Geiger | 2020 | ⭐⭐ | Youtube |
| Source | Course Name | Session | Difficulty | URL |
|---|---|---|---|---|
| AI Institute for Advances in Optimization | - | Causal Inference Course | 2023 | ⭐⭐⭐ |
| AI4OPT Seminar Series | - | Causal Inference Course | Spring 2023 | ⭐⭐⭐ |
| AI Institute for Advances in Optimization | - | AI4OPT Tutorial Lectures | 2021 | ⭐⭐⭐ |
| Source | Course Name | URL |
|---|---|---|
| Weights and Biases | Effective MLOps: Model Development | URL |
| Weights and Biases | CI/CD for Machine Learning (GitOps) | URL |
| Weights and Biases | Data Validation in Production ML Pipelines | URL |
| DeepLearning.AI | Machine Learning Engineering for Production (MLOps) | Youtube |
| Source | Course Code | Course Name | Session | Difficulty | URL |
|---|---|---|---|---|---|
| Imperial College, London | - | Neuroscience for machine learners | 2023 | ⭐ | Website Youtube |
| Neuromatch | - | Computational Neuroscience | - | ⭐ | Website |
| CAJAL Advanced Neuroscience Training | - | Computational Neuroscience | - | ⭐⭐ | Website |
| INCF | Computational Neuroscience | ⭐ | Website | ||
| University of Washington | Computational Neuroscience | ⭐ | Coursera (Free Audit) | ||
| Human Information Processing Lab | How to build a brain from scratch | - | ⭐⭐ | Website | |
| - | - | Data Science and Data Skills for Neuroscientists | - | ⭐⭐ | Website |
| - | - | Cosyne Tutorial 2022 - Spiking Neural Networks | 2022 | ⭐⭐ | Website |
| Source | Course Code | Course Name | Session | Difficulty | URL |
|---|---|---|---|---|---|
| NYU | PSYCH-GA 3405.004 / DS-GS 1016 | Computational cognitive modeling (Brenden Lake) | Spring 2022 | ⭐⭐⭐ | Website |
| NYU | PSYCH-GA 3405.001 | Categories and Concepts (Brenden Lake) | Fall 2021 | ⭐⭐ | Website |
| NYU | PSYCH-UA.46 | LAB IN COGNITION AND PERCEPTION (Brenden Lake) | Fall 2021 | ⭐⭐ | Website |
| Unversity of Washington | Computational Neuroscience | ⭐⭐ | Coursera |
| Source | Course Code | Course Name | Session | Difficulty | URL |
|---|---|---|---|---|---|
| University of Tübingen | - | Trustworthy Machine Learning | Winter 2023/2024 | ⭐⭐ | Youtube |
| Source | Course Code | Course Name | Session | Difficulty | URL |
|---|---|---|---|---|---|
| Hugging Face | Audio Processing | ⭐ | Link (Free) | ||
| The State Unversity of New York | Practical Time Series Analysis | ⭐⭐ | Coursera |
| Source | Course Code | Course Name | Session | Difficulty | URL |
|---|---|---|---|---|---|
| Stanford | - | Introduction to Statistics | N/A | ⭐ | Coursera (Free) |
| Harvard | Statistics 110 | 2015 | ⭐ | Youtube | |
| University of London | Probability and Statistics | Probability and Statistics: To p or not to p? | ⭐ | Coursera - Free Audit | |
| University of Zurich | An Intuitive Introduction to Probability | ⭐ | Coursera - Free Audit | ||
| University of Tübingen | - | Essential Statistics – Philipp Berens | 2020/21 | ⭐⭐ | Youtube |
| University of Tübingen | - | Statistical Machine Learning — Ulrike von Luxburg | 2020 | ⭐⭐ | Youtube |
| Source | Course Code | Course Name | Session | Difficulty | URL |
|---|---|---|---|---|---|
| UC Berkeley | CS 294 | Deep Unsupervised Learning | Spring 2020 | ⭐⭐⭐ | Youtube |
| Serrano.Academy | - | Unsupervised Learning | - | ⭐⭐ | Youtube |
| Source | Course Code | Course Name | Session | Difficulty | URL |
|---|---|---|---|---|---|
| Harvard | - | Explainable Artificial Intelligence | Spring 2023 | ⭐ | Course Website |
| Kaggle | Machine Learning Explainability | Extract human-understandable insights from any model. | URL | ||
| Stanford | Workshop | ML Explainability by Professor Hima Lakkaraju | N/A | ⭐⭐ |
���️ Only few selected resourses from only few selected topics are presented here in this page. To get access to all resources, check topic list an go to topic wise pages. ⬆️ CLICK HERE ⬆️
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