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.
Machine Learning and Artificial Intelligence
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
Google
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
Google
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)
Google
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
Data Analysis + Data Science
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)
Google
Data Science with Python
URL
Harvard
High-Dimensional Data Analysis
-
⭐
Course Website
University of Tübingen
-
Neural Data Science — Philipp Berens
2021
⭐⭐
Youtube
Natural Language Processing
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
Robotics and Autonomous Systems
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
Computational Neuroscience and ML
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
Trustworthiness and Fairness in Machine Learning
Source
Course Code
Course Name
Session
Difficulty
URL
University of Tübingen
-
Trustworthy Machine Learning
Winter 2023/2024
⭐⭐
Youtube
Time Series/Audio/Speech Processing
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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