We first built the list in 2020 using a data-driven approach that we have used each year, including 2025. You can find the methodology below.

Methodology

First, we identified the leading world university rankings. Since we were interested in computer science specifically, he looked at their latest computer science rankings.

For the 2025 update, Suparn, my colleague used the same sources and methodology to find the top 60 universities offering online computer science courses.

Here are the rankings used:

• QS: World University Ranking 2025 — Computer Science & IT

• Times Higher Education: World University Ranking 2025 — Computer Science

Then, we crawled and scraped each ranking.

Now that we had some data, we used Jupyter with Python to process it. We combined the two rankings into one by averaging the position of each university in each ranking. Then, we filtered out the universities that didn’t offer online courses, and limited the list to the top 60 institutions — the cream of the crop.

Combined ranking: top 10 universities for studying computer science in 2024

As you can see above, we found that the top three institutions are #1 MIT,  #2 Oxford , and #3 Stanford.

Finally, we used the Class Central database, with its 250K online courses, to find all the computer science courses offered by the universities in the ranking.

The end result is a list of 1000+ online courses offered by the 60 best universities in the world for studying computer science in 2025.

Stats

• Enrollments range from 10 to over 13 million, with 14 courses exceeding 1 million enrollments

• Altogether, they have over 81M enrollments, with an average of 222K enrollments

• 921 courses are in English, 114 Chinese, 5 Spanish, and 3 French

• Together, they account for more than 78K reviews at Class Central, with an average of 216 reviews

• Average rating: 4.37 out of 5.0

• All these courses are free or can be audited for free

• 104 courses are beginner level, 111 are intermediate level, and 49 are advanced level.

More Courses

The full list is split into subjects. With over 1000 courses to pick from, I hope you find something you like. But if these aren’t enough, check out Class Central’s catalog of 250K online courses or our thematic collections:

• 2000+ Free Developer and IT Certifications

• Harvard Computer Science Courses with Free Certificate

• 40+ Free Certificates from Wolfram U

• 800+ Free Certificates: Master Tech & Soft Skills with CodeSignal Learn

• 10,000+ Free Courses from Tech Giants: Learn from Google, Microsoft, Amazon, and More

• 8000+ OpenCourseWare Courses from Top Institutions.

Computer Science (727)

• Harvard CS50 – Full Computer Science University Course from Harvard University ★★★★★(1012)

• CS50's Introduction to Computer Science from Harvard University ★★★★★(204)

• Artificial Intelligence from Massachusetts Institute of Technology ★★★★★(182)

• Introduction to Electrical Engineering and Computer Science I from Massachusetts Institute of Technology ★★★★★(58)

• Advanced Algorithms - COMPSCI 224 from Harvard University ★★★★★(35)

• Introduction to Artificial Intelligence from Stanford University ★★★★☆(31)

• Machine Learning with Python: from Linear Models to Deep Learning. from Massachusetts Institute of Technology ★★★☆☆(30)

• CS50's Introduction to Artificial Intelligence with Python from Harvard University ★★★★★(29)

• Data Structures & Algorithms I: ArrayLists, LinkedLists, Stacks and Queues from Georgia Institute of Technology ★★★★★(28)

• Introduction to Algorithms from Massachusetts Institute of Technology ★★★★★(25)

• CS50’s Computer Science for Business from Harvard University ★★★★★(21)

• Intro to Machine Learning from Stanford University ★★★★☆(20)

• Automata Theory from Stanford University ★★★★☆(20)

• Introduction to Deep Learning from Massachusetts Institute of Technology ★★★★★(20)

• Computer Science 101 from Stanford University ★★★★☆(19)

• Cryptocurrency Engineering and Design from Massachusetts Institute of Technology ★★★★★(19)

• Human-Computer Interaction I: Fundamentals & Design Principles from Georgia Institute of Technology ★★★★★(15)

• How to Code: Simple Data from The University of British Columbia ★★★★☆(15)

• Design of Computer Programs from Stanford University ★★★★☆(14)

• Unix Tools: Data, Software and Production Engineering from Delft University of Technology ★★★★★(13)

• AI for Clinical Trials and Precision Medicine - Ruishan Liu from Stanford University ★★★★★(11)

• Learning from Data (Introductory Machine Learning course) from California Institute of Technology ★★★★★(10)

• CS50's Computer Science for Lawyers from Harvard University ★★★★★(8)

• Information and Communication Technology (ICT) Accessibility from Georgia Institute of Technology ★★★★☆(8)

• Reinforcement Learning from Brown University ★★★☆☆(8)

• Machine Learning Fundamentals from University of California, San Diego ★★★★☆(8)

• Introduction to Computer Science and Programming in Python from Massachusetts Institute of Technology ★★★★★(8)

• Machine Learning from Georgia Institute of Technology ★★★★☆(7)

• Introduction to Computer Vision from Georgia Institute of Technology ★★★★★(7)

• Data Structures & Algorithms II: Binary Trees, Heaps, SkipLists and HashMaps from Georgia Institute of Technology ★★★★★(7)

• Performance Engineering of Software Systems from Massachusetts Institute of Technology ★★★★★(7)

• Convolutional Neural Networks for Visual Recognition from Stanford University ★★★★☆(7)

• Advanced Operating Systems from Georgia Institute of Technology ★★★★★(6)

• Introduction to Computer Architecture from Carnegie Mellon University ★★★★★(6)

• Computer Networking from Georgia Institute of Technology ★★★★☆(6)

• Data Structures & Algorithms III: AVL and 2-4 Trees, Divide and Conquer Algorithms from Georgia Institute of Technology ★★★★★(6)

• Machine Learning for Healthcare from Massachusetts Institute of Technology ★★★★★(6)

• MIT 6.824 Distributed Systems - Spring 2020 from Massachusetts Institute of Technology ★★★★★(6)

• Data Structures: An Active Learning Approach from University of California, San Diego ★★★★★(5)

• How to Code: Complex Data from The University of British Columbia ★★★★★(5)

• Data Structures & Algorithms IV: Pattern Matching, Dijkstra’s, MST, and Dynamic Programming Algorithms from Georgia Institute of Technology ★★★★★(5)

• Distributed Systems from University of Cambridge ★★★★★(5)

• Electrical and Computer Engineering - Systems Programming and Concurrency from University of Waterloo ★★★★☆(5)

• Human-Computer Interaction II: Cognition, Context & Culture from Georgia Institute of Technology ★★★★★(4)

• 6.S191: Introduction to Deep Learning from Massachusetts Institute of Technology ★★★★☆(4)

• Introduction to Computational Thinking and Data Science from Massachusetts Institute of Technology ★★★★★(4)

• AI and Education Summit: Transforming Learning and Workforce Readiness from Stanford University ★★★★★(4)

• Software Architecture & Design from Georgia Institute of Technology ★★★★★(3)

• Language, Proof and Logic from Stanford University ★★★★☆(3)

• Machine Learning: Unsupervised Learning from Brown University ★★★☆☆(3)

• Algorithmic Design and Techniques from University of California, San Diego ★★★★☆(3)

• Using Electronic Health Records for Better Care from Stanford University ★★★★★(3)

• Design & AI - Envisioning AI-Augmented Products from Stanford University ★★★★★(3)

• Stanford Seminar - Security and the Software Defined Network from Stanford University ★★★★☆(3)

• Stanford Seminar - Self-Driving Cars for Everyone from Stanford University ★★★★★(3)

• Stanford Seminar - Practical Blockchain Applications - Steven Pu from Stanford University ★★★★★(3)

• Stanford Webinar - IOT: From Smart Sensors to Smart Networks from Stanford University ★★★★★(3)

• Stanford Seminar - Gender Disparities in Software Engineering from Stanford University ★★★★☆(3)

• Introduction to Operating Systems from Georgia Institute of Technology ★★★★★(2)

• Human-Computer Interaction IV: Evaluation, Agile Methods & Beyond from Georgia Institute of Technology ★★★★★(2)

• Deep Learning for Natural Language Processing from University of Oxford ★★★★★(2)

• Software Engineering: Introduction from The University of British Columbia ★★★☆☆(2)

• Knowledge-Based AI: Cognitive Systems from Georgia Institute of Technology ★★★☆☆(2)

• The Quantum Internet and Quantum Computers: How Will They Change the World? from Delft University of Technology ★★★☆☆(2)

• 6.S094: Deep Learning for Self-Driving Cars from Massachusetts Institute of Technology ★★★★☆(2)

• Introduction to Algorithms (SMA 5503) from Massachusetts Institute of Technology ★★★★★(2)

• Introduction to Computer Science and Programming (Fall 2008) from Massachusetts Institute of Technology ★★★★★(2)

• Stanford - Human-Computer Interaction Seminar Series from Stanford University ★★★★★(2)

• How to Compute with Schrödinger's Cat - An Introduction to Quantum Computing from Stanford University ★★★★☆(2)

• Web3 Considered - Possible Futures for Decentralization and Digital Ownership from Stanford University ★★★★☆(2)

• Race, Technology, and Algorithmic Bias - Vision and Justice from Harvard University ★★★★★(2)

• Deep Learning Lecture Series from University College London ★★★★★(2)

• Towards Unsupervised Biomedical Image Segmentation Using Hyperbolic Representations - Jeffrey Gu from Stanford University ★★★★☆(2)

• Creating Human-Computer Partnerships - Stanford Seminar from Stanford University ★★★★★(2)

• Stanford Seminar - Decision Transformer: Reinforcement Learning via Sequence Modeling from Stanford University ★★★★★(2)

• Computability, Complexity & Algorithms from Georgia Institute of Technology ★★★★★(1)

• High Performance Computer Architecture from Georgia Institute of Technology ★★★★★(1)

• Health Informatics: A Current and Historical Perspective from Georgia Institute of Technology ★★★★★(1)

• Human-Computer Interaction III: Ethics, Needfinding & Prototyping from Georgia Institute of Technology ★★★★★(1)

• Compilers from Stanford University ★★★★★(1)

• Introduction to Graduate Algorithms from Georgia Institute of Technology ★★★★★(1)

• Software Construction: Object-Oriented Design from The University of British Columbia ★★☆☆☆(1)

• Introduction to Scientific Machine Learning from Purdue University ★★★★☆(1)

• Software Construction: Data Abstraction from The University of British Columbia ★★★☆☆(1)

• Machine Learning for Semiconductor Quantum Devices from Delft University of Technology ★★★★☆(1)

• Data Structures and Algorithm Design Part II | 数据结构与算法设计(下) from Tsinghua University ★★★★★(1)

• Introduction to Computational Science and Engineering from Massachusetts Institute of Technology ★★★★☆(1)

• Nature, in Code: Biology in JavaScript from École Polytechnique Fédérale de Lausanne ★★★☆☆(1)

• Introduction to Algorithms from Massachusetts Institute of Technology ★★★★☆(1)

• Mathematics for Computer Science from Massachusetts Institute of Technology ★★★★☆(1)

• Design and Analysis of Algorithms from Massachusetts Institute of Technology ★★★★★(1)

• Introduction to Computer Science and Programming (Spring 2011) from Massachusetts Institute of Technology ★★★★★(1)

• Boltzmann Law: Physics to Computing from Purdue University ★★★★☆(1)

• Deep Learning for Computer Vision from University of Michigan ★★★★★(1)

• Quantum Computer Systems Design III: Working with Noisy Systems from The University of Chicago ★★★★☆(1)

• Understanding the World Through Data from Massachusetts Institute of Technology ★★★★☆(1)

• Learning and Memory from Yale University ★★★★★(1)

• Beyond Cryptocurrency: Blockchain for the Real World from Stanford University ★★★★☆(1)

• Dive Into the World of Blockchain: Principles, Mechanics, and Tokens from University of Toronto ★★★☆☆(1)

• Rethinking the AI-UX Boundary for Designing Human-AI Experiences from Stanford University ★★★★★(1)

• How ChatGPT and Generative AI Will Shape the Future of Work from Stanford University ★☆☆☆☆(1)

• Stanford Seminar - How Not to Generate Random Numbers from Stanford University ★★★★★(1)

• Stanford Seminar - Building the Smartest and Open Virtual Assistant to Protect Privacy - Monica Lam from Stanford University ★★★★★(1)

• Tuning GPT-3 on a Single GPU via Zero-Shot Hyperparameter Transfer from Massachusetts Institute of Technology ★★★★★(1)

• MIT: Recurrent Neural Networks from Alexander Amini ★★★★★(1)

• Wireless Above 100GHz from New York University (NYU) ★★★☆☆(1)

• Generalization and Personalization in Federated Learning - Karan Singhal from Stanford University ★★★★★(1)

• MedAI- Graph-Based Modeling in Computational Pathology - Siyi Tang from Stanford University ★★★★★(1)

• Synthetic Data Sets for the Financial Industry from Stanford University ★★★★☆(1)

• Learning Controllers - From Engineering to AGI from Massachusetts Institute of Technology ★★★★☆(1)

• Stanford Seminar - Google's Multilingual Neural Machine Translation System from Stanford University ★★★★★(1)

• Deep Generative Modeling from Alexander Amini ★★★★★(1)

• Regulatory Evaluation of Image Processing Software Devices from Yale University ★★★★★(1)

• Edge Computing in Autonomous Vehicles - Panel Discussion from Stanford University ★★★★☆(1)

• Finding Fairness - Incorporating Societal Values in Computer Algorithms from Harvard University ★★★☆☆(1)

• Exploring the Implications of Machine Learning for Cognitive Disabilities from Stanford University ★★★★★(1)

• AI in Molecular Imaging from Yale University ★★★★★(1)

• Stanford Seminar - Toward Scalable Autonomy - Aleksandra Faust from Stanford University ★★★★☆(1)

• Pioneering the Science of Information - IBM's Role in Shaping Information Technology from University of Melbourne ★★★★★(1)

• The Soul of a New Machine - Rethinking the Computer from Stanford University ★★★★☆(1)

• Data Compression I - Lecture 2: Prefix Free Codes from Stanford University ★★★★★(1)

• Feedback Control from Pixels from Massachusetts Institute of Technology ★★★☆☆(1)

• Visualization for Machine Learning - Google Brain from Alexander Amini ★★★★★(1)

• The Information Age and The Age of Creation from Stanford University ★★★★★(1)

• Algorithms: Design and Analysis, Part 1 from Stanford University

• Cyber-Physical Systems Design & Analysis from Georgia Institute of Technology

• Artificial Intelligence from Georgia Institute of Technology

• Introduction to Quantum Computing from The University of British Columbia

• Human-Computer Interaction from Georgia Institute of Technology

• Hacker Tools from Massachusetts Institute of Technology

• High Performance Computing from Georgia Institute of Technology

• Programming & Data Structures from Columbia University

• AI for Teacher Assistance from Georgia Institute of Technology

• Algorithms: Design and Analysis, Part 2 from Stanford University

• Introduction to Blockchain Technology and Applications from University College London

• Machine Learning and AI with Python from Harvard University

• AI skills: Introduction to Unsupervised, Deep and Reinforcement Learning from Delft University of Technology

• Health Informatics: Data and Interoperability Standards from Georgia Institute of Technology

• Development and Applications of Germanium Quantum Technologies from Delft University of Technology

• Ethics in AI Design from Delft University of Technology

• Computer Applications of Artificial Intelligence and e-Construction from Purdue University

• AI skills for Engineers: Supervised Machine Learning from Delft University of Technology

• AI at the Edge on Arm: Understanding and Deploying LLMs for Mobile Devices from Arm Education

• Compilers: Theory and Practice from Georgia Institute of Technology

• Image Processing and Analysis for Life Scientists from École Polytechnique Fédérale de Lausanne

• Assessment Design with AI from Georgia Institute of Technology

• Introduction to Deep Learning from Purdue University

• Chatbots for Instruction from Georgia Institute of Technology

• Complex Problem Structuring: a Socio-Technical Perspective from Delft University of Technology

• Machine Learning from Georgia Institute of Technology

• Health Informatics: The Cutting Edge from Georgia Institute of Technology

• Computer Graphics II: Rendering from University of California, San Diego

• Computer Graphics from University of California, San Diego

• IoT Systems and Industrial Applications with Design Thinking from École Polytechnique Fédérale de Lausanne

• Introduction to Quantum Computing for Everyone from The University of Chicago

• Statistical Machine Learning from Carnegie Mellon University

• Fundamentals of Quantum Information from Delft University of Technology

• Applied Quantum Computing I: Fundamentals from Purdue University

• Accessible Gamification from Georgia Institute of Technology

• Applied Quantum Computing III: Algorithm and Software from Purdue University

• Data Structures Fundamentals from University of California, San Diego

• Computer Vision for Embedded Systems from Purdue University

• Graph Algorithms from University of California, San Diego

• Self-Driving Cars with Duckietown from ETH Zurich

• Machine Learning Use Cases in Finance from Université de Montréal

• Quantum Detectors and Sensors from Purdue University

• Fundamentals of TinyML from Harvard University

• Introduction to Quantum Computing for Everyone 2 from The University of Chicago

• Applications of Machine Learning in Plant Science from Cornell University

• Deep Learning Essentials from Université de Montréal

• Deploying TinyML from Harvard University

• Applied Quantum Computing II: Hardware from Purdue University

• MLOps for Scaling TinyML from Harvard University

• AI in Healthcare. Hype or Help? from KU Leuven University

• Quantum Communication and the Quantum Network Explorer from Delft University of Technology

• Quantum-safe Digital Infrastructures: Technical Challenges and Solutions from Delft University of Technology

• Applications of TinyML from Harvard University

• Quantum Computer Systems Design I: Intro to Quantum Computation and Programming from The University of Chicago

• Introduction à l'éthique de l’IA from Université de Montréal

• Bias and Discrimination in AI from Université de Montréal

• Explore Trading and Lending in Decentralized Finance from University of Toronto

• Quantum Computer Systems Design II: Principles of Quantum Architecture from The University of Chicago

• Data Structures and Algorithm Design Part I | 数据结构与算法设计(上) from Tsinghua University

• Quantum-safe Digital infrastructures: Challenges and Solutions for Governance from Delft University of Technology

• AI in Practice: Preparing for AI from Delft University of Technology

• LAFF-On Programming for High Performance from The University of Texas at Austin

• Modern Distributed Systems from Delft University of Technology

• GT - Refresher - Advanced OS from Georgia Institute of Technology

• Biais et discrimination en IA from Université de Montréal

• Techniques d’intelligence artificielle : des fondements aux applications from Université de Montréal

• The Society of Mind from Massachusetts Institute of Technology

• Introduction à l'apprentissage profond from Université de Montréal

• Advanced Data Structures from Massachusetts Institute of Technology

• NP-Complete Problems from University of California, San Diego

• String Processing and Pattern Matching Algorithms from University of California, San Diego

• Blockchain Technology and the Future of FinTech from University of Toronto

• Quantum Hardware and its Applications with Quantum Inspire from Delft University of Technology

• 计算几何 | Computational Geometry from Tsinghua University

• LAFF – On Programming for Correctness from The University of Texas at Austin

• L'essentiel de l'apprentissage profond from Université de Montréal

• Vision artificielle et exploitation intelligente des ressources naturelles from Université de Montréal

• Les coulisses des systèmes de recommandation from Université de Montréal

• Cryptocurrency Engineering and Design - Spring 2018 from Massachusetts Institute of Technology

• AI to Understand and Connect People from KU Leuven University

• AI in Practice: Applying AI from Delft University of Technology

• Big Data Machine Learning | 大数据机器学习 from Tsinghua University

• Introduction to Computers - Stanford University from Stanford University

• Navigating complex health data challenges from University of Cambridge

• Recommender Systems: Behind the Screen from Université de Montréal

• Navigating Decentralized Derivatives and Governance in Blockchain from University of Toronto

• How You Can Use ChatGPT to Increase Your Creative Output from Stanford University

• Optimization: principles and algorithms - Network and discrete optimization from École Polytechnique Fédérale de Lausanne

• Stanford CS229 - Machine Learning Full Course Taught by Andrew Ng - Autumn 2018 from Stanford University

• Deep Learning in Life Sciences - Spring 2021 from Massachusetts Institute of Technology

• 算法基础 | Fundamental Algorithms from Peking University

• Optimization: principles and algorithms - Unconstrained nonlinear optimization from École Polytechnique Fédérale de Lausanne

• Introduction to Computational Thinking from Massachusetts Institute of Technology

• Artificial Intelligence - Principles and Techniques - Autumn 2019 from Stanford University

• Introduction to EECS I from Massachusetts Institute of Technology

• The Battlecode Programming Competition from Massachusetts Institute of Technology

• Geometric Folding Algorithms: Linkages, Origami, Polyhedra from Massachusetts Institute of Technology

• AI and Law: Implications for Society and Legal Systems from Stanford University

• Artificial Intelligence for Business Leaders from Stanford University

• Programming for the Puzzled (January IAP 2018) from Massachusetts Institute of Technology

• Stanford Seminar - Software-Defined Networking at the Crossroads from Stanford University

• Information Technology as an Integrating Force in Manufacturing from Massachusetts Institute of Technology

• Multicore Programming Primer from Massachusetts Institute of Technology

• 理论计算机科学基础 | Introduction to Theoretical Computer Science from Peking University

• Artificial Intelligence - Current and Future Paradigms and Implications from Stanford University

• Theory of Computation, Fall 2020 from Massachusetts Institute of Technology

• Cryptocurrencies and Blockchains - The Science Behind the Technology from Stanford University

• Introduction to CS and Programming using Python from Massachusetts Institute of Technology

• Stanford Seminar - Deep Learning for Medical Diagnoses from Stanford University

• Stanford Seminar - Transformers in Language: The Development of GPT Models Including GPT-3 from Stanford University

• Introduction to the Theory of Computing - Stanford from Stanford University

• Stanford Seminar - The FATE of AI Ethics, Anna Bethke from Stanford University

• Stanford Seminar - Designing Assistive Technologies for Agency from Stanford University

• Algorithmic Lower Bounds: Fun with Hardness Proofs from Massachusetts Institute of Technology

• Software-Centric Visible Light Communication for the Internet of Things from Stanford University

• How to Use ChatGPT to Increase Your Creative Output - Part Two from Stanford University

• MIT Introduction to Deep Learning from Alexander Amini

• Stanford CS330: Deep Multi-Task and Meta Learning from Stanford University

• Machine Learning Course from California Institute of Technology

• Using Data for Increased Realism with Haptic Modeling and Devices from Stanford University

• Stanford Seminar - Deep Learning in Speech Recognition from Stanford University

• The Future of Blockchain and Cryptocurrencies - Dan Boneh from Stanford University

• AI in the Workplace: Rethinking Skill Development from Stanford University

• AI Bias and Fairness from Alexander Amini

• Stanford Seminar - Natural Language Processing for Conversational Interfaces from Stanford University

• Stanford Seminar - Bugs in Crypto Implementations from Stanford University

• The Artful Design of Interactive AI Systems from Stanford University

• Artificial Intelligence Planning from University of Edinburgh

• Deep Generative Modeling from Alexander Amini

• MIT: Reinforcement Learning from Alexander Amini

• How AI Is Turning Your Smartphone Into the Swiss Army Knife of Clinical Diagnostics from Yale University

• Stanford CS234: Reinforcement Learning - Winter 2019 from Stanford University

• Stanford Seminar - Representation Learning for Autonomous Robots, Anima Anandkumar from Stanford University

• Stanford Seminar - Creating Interfaces with Rich Physical Properties Through Digital Fabrication from Stanford University

• How Machine Learning is Changing Software from Stanford University

• AI in Healthcare from Alexander Amini

• Machine Learning with Graphs - Fall 2019 from Stanford University

• Stanford Seminar - Enabling NLP, Machine Learning, and Few-Shot Learning Using Associative Processing from Stanford University

• Smart Physical Systems from the Standpoint of an AI Company from Stanford University

• Stanford Seminar - Can the Brain Do Back-Propagation? from Stanford University

• Frontiers of Medical AI - Therapeutics and Workflows from Stanford University

• Agentic AI: A Progression of Language Model Usage from Stanford University

• Data Augmentation for Image-Based Reinforcement Learning from Massachusetts Institute of Technology

• MIT EI Seminar - Phillip Isola - Emergent Intelligence- Getting More Out of Agents Than You Bake In from Massachusetts Institute of Technology

• MIT 6.S191 - Automatic Speech Recognition from Alexander Amini

• Theory of Computation from Massachusetts Institute of Technology

• 算法设计与分析(高级) | Advanced Design and Analysis of Algorithms from Peking University

• Deep Maths - Machine Learning and Mathematics from University of Oxford

• Forecasting and Aligning AI - Jacob Steinhardt from Stanford University

• Deep Learning for Symbolic Mathematics - Guillaume Lample & Francois Charton from Stanford University

• Stanford Seminar - Neural Networks on Chip Design from the User Perspective from Stanford University

• Solana Larsen- Who Has Power Over AI? from Stanford University

• Stanford Seminar - Rebooting the Internet from Stanford University

• Federated Learning in Medicine - Breaking Down Silos to Advance Medical Research from Stanford University

• Large Language Models - Will They Keep Getting Bigger? from Massachusetts Institute of Technology

• Jacob Andreas - Natural Language Explanations of Deep Networks from Massachusetts Institute of Technology

• Diverse Data and Efficient Algorithms for Robot Learning from Massachusetts Institute of Technology

• Towards AI for 3D Content Creation from Alexander Amini

• 6.036 Introduction to Machine Learning from Massachusetts Institute of Technology

• The Creativity Code - Marcus du Sautoy from University of Oxford

• Stanford Seminar - Training Classifiers with Natural Language Explanations from Stanford University

• Stanford Seminar - New Golden Age for Computer Architecture - John Hennessy from Stanford University

• Stanford Seminar - Applying Theory to Practice and Practice to Theory from Stanford University

• Weakly-Supervised, Large-Scale Computational Pathology for Diagnosis and Prognosis - Max Lu from Stanford University

• Federated Hyperparameter Tuning - Challenges, Baselines, and Connections from Stanford University

• Stanford Seminar - What's Next for Blockchain in China? from Stanford University

• Artificial Intelligence - The Future of Medicine and Health Care Is Here from Stanford University

• Neurosymbolic AI from Alexander Amini

• Beyond Deep Learning - Learning+Reasoning from Alexander Amini

• Sequence Modeling with Neural Networks from Alexander Amini

• Stanford Seminar - Lenia- Biology of Artificial Life, Bert Wang-Chak Chan from Stanford University

• Stanford Seminar - Scalable Intelligent Systems Build and Deploy by 2025 from Stanford University

• Stanford Seminar - Erudite: Prototype System for Computational Intelligence from Stanford University

• Stanford Seminar - Safe Passwords Made Easy to Use from Stanford University

• Computational Memory - A Stepping-Stone to Non-Von Neumann Computing? from Stanford University

• Stanford Seminar - The Quest for Low Storage Latency Changes Everything from Stanford University

• Stanford Seminar - The REX Neo Architecture - An Energy Efficient New Processor Architecture from Stanford University

• Stanford Seminar - Citadel of One- Individuality and the Rise of the Machines, Suzanne Sadedin from Stanford University

• Dynamic Code Optimization and the NVIDIA Denver Processor from Stanford University

• Stanford Seminar - Instruction Sets Should Be Free- The Case for RISC-V from Stanford University

• Multimodal Opportunistic Risk Assessment for Ischemic Heart Disease - Juan Manuel from Stanford University

• Rethinking Architecture Design for Data Heterogeneity in FL - Liangqiong Qu from Stanford University

• Few-Shot Chest X-Ray Diagnosis Using Clinical and Literature Images from Stanford University

• Data-Efficient AI for Accelerating MRI Acquisition from Stanford University

• Hacking AI - Security & Privacy of Machine Learning Models from Stanford University

• Stanford CS229 - Machine Learning - Summer 2019 - Kernel Methods & Support Vector Machine from Stanford University

• Create a Better User Experience Through AI - Michael Bernstein from Stanford University

• NVIDIA GPU Computing - A Journey from PC Gaming to Deep Learning from Stanford University

• AI Transforms Health Care - The Future of Medicine and Health Care from Stanford University

• Computer Science and IT at the University of Melbourne - Faculty of Engineering and IT Overview from University of Melbourne

• CS50 2012 - Week 3 Continued: Search Algorithms and Sorting Techniques from Harvard University

• EI Seminar - Recent Papers in Embodied Intelligence from Massachusetts Institute of Technology

• Computational Principles Underlying the Learning of Sensorimotor Repertoires from Massachusetts Institute of Technology

• Intuitive Reasoning as Unsupervised Neural Generation from Massachusetts Institute of Technology

• The Lottery Ticket Hypothesis - Michael Carbin from Massachusetts Institute of Technology

• Recurrent Neural Networks and Transformers from Alexander Amini

• MIT: Introduction to Deep Learning from Alexander Amini

• Introduction to Deep Learning from Alexander Amini

• Advanced Topics in Cryptography from Massachusetts Institute of Technology

• Stanford Seminar - Failures & Where to Find Them: Considering Safety as a Function of Structure from Stanford University

• Denoising Diffusion Models for Denoising Diffusion MRI - Tiange Xiang from Stanford University

• Real-Time Seizure Detection Using EEG - Hyewon Jeong from Stanford University

• Deep Learning Methods for Electrocardiograms and Echocardiograms from Stanford University

• Towards Generalist Imaging Using Multimodal Self-Supervised Learning - Mars Huang from Stanford University

• The New Role of Physics Simulation in AI from Stanford University

• Stanford Seminar - Human-AI Interaction Under Societal Disagreement from Stanford University

• Inequality in Healthcare: Using AI and Data Science to Reduce Disparities from Stanford University

• Stanford Seminar - Taraxa.io: A Globalized Blockchain Protocol Startup from Stanford University

• Cybersecurity in the Modern Era - Zero Knowledge Proofs Explained from Stanford University

• Generative AI for Multimodal Biomedicine from Stanford University

• Building Embodied Autonomous Agents from Massachusetts Institute of Technology

• Machine Learning for Scent from Alexander Amini

• Introduction to Deep Learning from Alexander Amini

• Convolutional Neural Networks from Alexander Amini

• Convolutional Neural Networks from Alexander Amini

• Introduction to Deep Learning - MIT 2018 from Alexander Amini

• Improved Ultrasound Image Formation - Domain Adaptation With No Data from Yale University

• ChatGPT pour tous from Université de Montréal

• Stanford Seminar - Leela: A Semantic Intelligent Agent from Stanford University

• Stanford Seminar - An Architect's Point of View on Emerging Technologies from Stanford University

• Stanford Seminar - Dialog Markets from Stanford University

• Stanford Seminar - Time Traveling Hardware and Software Systems from Stanford University

• Stanford Seminar - Concepts and Questions as Programs from Stanford University

• Voices in the Code - A Story About People, Their Values, and the Algorithm They Made from Stanford University

• Against Ethical Robots with Ron Chrisley from Stanford University

• Stanford Seminar - Is Google Search Dying? from Stanford University

• The Race for Technological Supremacy - Emerging Technologies and Global Implications from Stanford University

• Stanford Seminar - Fastai: A Layered API for Deep Learning from Stanford University

• Deep Learning - Class Introduction and Logistics - Lecture 1 from Stanford University

• How is AI Evolving? A Look at the Startup Landscape in the U.S. and Asia from Stanford University

• Entrepreneurial Lessons from Self-Driving Cars from Stanford University

• Stanford Seminar - Transformers United: Neuroscience-Inspired Artificial Intelligence from Stanford University

• Overview of Transformers - Stanford CS25: Lecture 1 from Stanford University

• Language Models as Temporary Training Wheels to Facilitate Learning from Stanford University

• Can You Trust a Computer Algorithm - How and Why from University of Melbourne

• Recent Papers in Embodied Intelligence I from Massachusetts Institute of Technology

• Deep Learning New Frontiers from Alexander Amini

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• Optimizing for Interpretability in Deep Neural Networks - Mike Wu from Stanford University

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• Databases: Relational Databases and SQL from Stanford University ★★★★☆(3)

• Cloud Computing - What’s on the Horizon with Dr. Timothy Chou from Stanford University ★★★★☆(3)

• CS50's Mobile App Development with React Native from Harvard University ★★★★★(2)

• Database Systems Concepts & Design from Georgia Institute of Technology ★★★★☆(1)

• Automated Software Testing: Model and State-based Testing from Delft University of Technology ★★★★★(1)

• Using JavaScript, JQuery, and JSON in Django from University of Michigan ★★★★★(1)

• Databases: Semistructured Data from Stanford University ★★★★★(1)

• Programación para todos (empezando con Python) from University of Michigan ★★★★☆(1)

• Seven Databases in Seven Weeks - Fall 2014 from Carnegie Mellon University ★★★★★(1)

• Advanced Database Systems - Spring 2019 from Carnegie Mellon University ★★★★★(1)

• Stanford Seminar - Optional Static Typing for Python from Stanford University ★★★★★(1)

• Advanced Database Systems - Spring 2017 from Carnegie Mellon University ★★★★☆(1)

• Rocket- Securing the Web at Compile-Time from Stanford University ★★★★★(1)

• Stanford Seminar - KUtrace from Stanford University ★★★★★(1)

• Stop User Errors - Mastering Usability Engineering in Medical Software Course from Yale University ★★★☆☆(1)

• Automated Software Testing: Unit Testing, Coverage Criteria and Design for Testability from Delft University of Technology

• Quantitative Methods for Biology from Harvard University

• CS 6340: Software Analysis & Testing from Georgia Institute of Technology

• Databases: Advanced Topics in SQL from Stanford University

• Database Design and Basic SQL in PostgreSQL from University of Michigan

• R Programming Fundamentals from Stanford University

• CS50's Introduction to Programming with R from Harvard University

• Creating Virtual Reality (VR) Apps from University of California, San Diego

• How Virtual Reality Works from University of California, San Diego

• Introduction to Object-Oriented Programming with Java III: Exceptions, Data Structures, Recursion, and GUIs from Georgia Institute of Technology

• Databases: Modeling and Theory from Stanford University

• Using JavaScript and JSON in Django from University of Michigan

• Mobile Application Experiences from Massachusetts Institute of Technology

• UML Class Diagrams for Software Engineering from KU Leuven University

• Worldbuilding for Video Games from The University of British Columbia

• Real-Time Audio Signal Processing in Faust from Stanford University

• JSON and Natural Language Processing in PostgreSQL from University of Michigan

• Global Software Development from Delft University of Technology

• Statistical Learning with Python from Stanford University

• Databases: OLAP and Recursion from Stanford University

• Introduction to Object-Oriented Programming with Java II: Object-Oriented Programming and Algorithms from Georgia Institute of Technology

• Introduction to Object-Oriented Programming with Java I: Foundations and Syntax Basics from Georgia Institute of Technology

• Intermediate PostgreSQL from University of Michigan

• Minecraft, Coding and Teaching from University of California, San Diego

• Programming Reactive Systems from École Polytechnique Fédérale de Lausanne

• Building Web Applications in Django from University of Michigan

• Database Architecture, Scale, and NoSQL with Elasticsearch from University of Michigan

• Web Application Technologies and Django from University of Michigan

• Introduction à la science des données sociales avec R from Université de Montréal

• Django Features and Libraries from University of Michigan

• MATLAB et Octave pour débutants from École Polytechnique Fédérale de Lausanne

• Estructuras de Datos con Python from University of Michigan

• C++ Programming | C++程序设计 from Peking University

• Java程序设计 | Java Programming from Peking University

• CS193p - Developing Apps for iOS from Stanford University

• Virtual Reality from University of Illinois at Urbana-Champaign

• How to Design Addictive Games from Stanford University

• iPhone Application Development Spring 2020 from Stanford University

• 程序设计基础 from Peking University

• Stanford Seminar - Programming Should Be More Than Coding from Stanford University

• Stanford Seminar - Stories from CoCoLab: Probabilistic Programs, Cognitive Modeling, & Smart Web Pages from Stanford University

• Ciencia de Datos: Fundamentos de R from Harvard University

• Extended Reality for Everybody - Michael Nebeling from Stanford University

• CS 241: System Programming from University of Illinois at Urbana-Champaign

• C++语言程序设计基础 from Tsinghua University

• Stanford Seminar - Accessible Virtual Reality for People with Limited Mobility from Stanford University

• Haskell: Lecture notes and assignments from University of Pennsylvania

• Learn Computer Science Online from University of Illinois at Urbana-Champaign

• Computer Language Engineering (SMA 5502) from Massachusetts Institute of Technology

• Advanced Database Systems - Spring 2016 from Carnegie Mellon University

• User Experience (UX) Design: Human Factors and Culture in Design from Tsinghua University

• Intro to Database Systems - Fall 2017 from Carnegie Mellon University

• Stanford Seminar - Mind Your State for Your State of Mind from Stanford University

• Making Teamwork an Objective Discipline - Sid Sijbrandij CEO & Chairman of GitLab from Stanford University

• Stanford Seminar - Concatenative Programming- From Ivory to Metal from Stanford University

• Programming Languages - The Fundamental Tools of the Computer Age from University of Melbourne

• Intro to Database Systems - Fall 2018 from Carnegie Mellon University

• Advanced Database Systems - Spring 2018 from Carnegie Mellon University

• Paul's Disciples - Pseudepigraphic Letters to Colossians and Ephesians from Yale University

• LaTeX course from University of Amsterdam

• Intro to Database Systems - Fall 2021 from Carnegie Mellon University

• Advanced Database Systems - Spring 2020 from Carnegie Mellon University

• Hardware Accelerated Database Lectures - Fall 2018 from Carnegie Mellon University

• 程序设计基础 from Tsinghua University

• 学做小程序——基础篇 from Tsinghua University

• 计算机程序设计基础 from Tsinghua University

• Stanford Seminar - The Future of Edge Computing from an International Perspective from Stanford University

• Missing Semester IAP 2020 from Massachusetts Institute of Technology

• Structure and Interpretation of Computer Programs from Massachusetts Institute of Technology

• Internet Technology in Local and Global Communities from Massachusetts Institute of Technology

• Intro to Database Systems - Fall 2019 from Carnegie Mellon University

• Time Series Database Lectures from Carnegie Mellon University

• The Databaseology Lectures - Fall 2015 from Carnegie Mellon University

• PACS & AI – From Integration to Cloud from Yale University

• Java程序设计 from Tsinghua University

• Designing the Interactive Paper from Stanford University

• Stanford Seminar - PyWren - Pushing Microservices to Teraflops from Stanford University

• Living in Information Everywhere from Stanford University

• Wearing a VR Headset While Driving to Improve Vehicle Safety from Stanford University

• Master Code Management - Essential Revision Control Systems for Medical Software Course from Yale University

• Software Testing Essentials - Finding Bugs and Ensuring Quality in Medical Software - 8.3 from Yale University

• C++语言程序设计进阶 from Tsinghua University

• 学做小程序——实战篇：树洞小程序 from Tsinghua University

• 学做小程序--云开发篇：近义词小程序 from Tsinghua University

• Jeremy Bailenson: Your Mind on the Metaverse from Stanford University

• Stanford Seminar - TechFail: From Intersectional Inaccessibility to Inclusive Design from Stanford University

• Understanding the Utility of Haptic Feedback in Telerobotic Devices from Stanford University

• Stanford Seminar - I Forgot, I Invented Hypertext - Ted Nelson from Stanford University

• Edge Computing and the Evolution of AR-VR - Panel Discussion from Stanford University

• Stanford Seminar - Virtual & Mixed Reality for Security of Critical City-Scale Cyber-Physical Systems from Stanford University

• Graph Analysis of Russian Twitter Trolls Using Neo4j from Stanford University

• An Alternative to the American Way of Innovation from Stanford University

• Integrating Interactive Devices with the User's Body from Stanford University

• Beyond Benchmarks – Building a Science of AI Measurement from Stanford University

• VR/AR in IR - Mixed Reality in Medicine from Yale University

• JAVA程序设计进阶 from Tsinghua University

• Web前端攻城狮 from Tsinghua University

• 基于R语言的社会统计分析 from Tsinghua University

• Making the Invisible Visible - Observing Complex Software Dynamics from Stanford University

• Stanford Seminar - Robosion: Software Platform for Lifelike Humanoids from Stanford University

• From Flat to Phantasmal: Enhancing User Experiences Through Spatial Computing Advancements from Stanford University

• Essential Medical Software Verification and Testing Strategies - 8.5 from Yale University

• VC++面向对象与可视化程序设计（上）：Windows编程基础 from Tsinghua University

• VC++面向对象与可视化程序设计（下）：MFC编程基础 from Tsinghua University

• 汇编语言程序设计 from Tsinghua University

• 基于Linux的C++ from Tsinghua University

• JAVA程序设计进阶 from Tsinghua University

• 面向对象程序设计（C++） from Tsinghua University

• 游戏程序设计 from Tsinghua University

• C++编程训练营 from Tsinghua University

• 低时延网络挑战赛：从5G和边缘计算到互联网 from Tsinghua University

• 界面设计导论 from Tsinghua University

• 高级数据库系统 from Tsinghua University

• 游戏分析与评测 from Tsinghua University

Data Science (110)

• Fundamentals of Qualitative Research Methods from Yale University ★★★★★(187)

• The Analytics Edge from Massachusetts Institute of Technology ★★★★★(80)

• Next in Data Visualization - Michelle Borkin - Radcliffe Institute from Harvard University ★★★★☆(61)

• Python for Data Science from University of California, San Diego ★★★★☆(48)

• Probability and Statistics in Data Science using Python from University of California, San Diego ★★☆☆☆(32)

• Introduction to Computational Thinking and Data Science from Massachusetts Institute of Technology ★★★★★(30)

• Mining Massive Datasets from Stanford University ★★★★★(24)

• Statistics and R from Harvard University ★★★★☆(20)

• DCO042 - Python For Informatics from University of Michigan ★★★★★(14)

• Data Science: R Basics from Harvard University ★★★★★(12)

• Foundations of Data Analysis - Part 1: Statistics Using R from The University of Texas at Austin ★★★★☆(8)

• Algorithms for Big Data from Harvard University ★★★★★(8)

• Data Analysis Essentials from Imperial College London ★☆☆☆☆(7)

• Data Analytics at the Exascale for Free Electron Lasers Project from Stanford University ★★★★★(7)

• Data Science: Visualization from Harvard University ★★★★★(6)

• Computing for Data Analysis from Georgia Institute of Technology ★★★☆☆(6)

• Introduction to Analytics Modeling from Georgia Institute of Technology ★★★★☆(5)

• STAT115 2020 from Harvard University ★★★★★(4)

• The Analytics Edge (Spring 2017) from Massachusetts Institute of Technology ★★★★★(4)

• Stanford Seminar - Secure Data Science on the Internet of Things from Stanford University ★★★★☆(4)

• Causal Diagrams: Draw Your Assumptions Before Your Conclusions from Harvard University ★★★★★(3)

• Data Science: Building Machine Learning Models from Harvard University ★★★★☆(3)

• High-Dimensional Data Analysis from Harvard University ★★★★☆(3)

• Introduction to Data Science with Python from Harvard University ★★★★☆(3)

• Data Analysis: Statistical Modeling and Computation in Applications from Massachusetts Institute of Technology ★★★★★(2)

• Data Science: Probability from Harvard University ★★★★☆(2)

• Data Science: Wrangling from Harvard University ★★★★★(2)

• AI Skills for Engineers: Data Engineering and Data Pipelines from Delft University of Technology ★★★★☆(2)

• Stanford Seminar - When DNA Meets AI from Stanford University ★★★★★(2)

• Stanford Seminar - New Platforms for Development Solutions from Stanford University ★★★★★(2)

• Data Analytics in Health – From Basics to Business from KU Leuven University ★★★★★(1)

• Data Science: Productivity Tools from Harvard University ★★★★★(1)

• Data Science: Inference and Modeling from Harvard University ★★★★☆(1)

• Policy Analysis Using Interrupted Time Series from The University of British Columbia ★★★★★(1)

• STAT115 2018 from Harvard University ★★★★★(1)

• Big Data Analytics from California Institute of Technology ★★★☆☆(1)

• MIT Deep Learning in Life Sciences Spring 2020 from Massachusetts Institute of Technology ★★★★★(1)

• MIT CompBio Course Projects Fall 2019 from Massachusetts Institute of Technology ★★★★☆(1)

• Soccermatics - Could a Premier League Team One Day Be Managed by a Mathematician? from University of Oxford ★★★★★(1)

• Data and Visual Analytics from Georgia Institute of Technology

• Principles, Statistical and Computational Tools for Reproducible Data Science from Harvard University

• Data Science: Capstone from Harvard University

• CSE 8803 Special Topics: Big Data from Georgia Institute of Technology

• Introduction to Genomic Data Science from University of California, San Diego

• Foundations of Data Analytics from The Hong Kong University of Science and Technology

• Statistical Computing with R - a gentle introduction from University College London

• Data Analytics for Business from Georgia Institute of Technology

• Dynamic Programming: Applications In Machine Learning and Genomics from University of California, San Diego

• Algorithms and Data Structures Capstone from University of California, San Diego

• Graph Algorithms in Genome Sequencing from University of California, San Diego

• Big Data for Reliability and Security from Purdue University

• Data Creation and Collection for Artificial Intelligence via Crowdsourcing from Delft University of Technology

• Big Data Computing with Spark from The Hong Kong University of Science and Technology

• Advanced Big Data Systems | 高级大数据系统 from Tsinghua University

• Data Mining and Knowledge Discovery from The Hong Kong University of Science and Technology

• Mathematical Methods for Data Analysis from The Hong Kong University of Science and Technology

• Data Analysis: Building Your Own Business Dashboard from Delft University of Technology

• Big Data Analytics Using Spark from University of California, San Diego

• Big Data Technology Capstone Project from The Hong Kong University of Science and Technology

• Big Data Technology Capstone Project from The Hong Kong University of Science and Technology

• Big Data Technology Capstone Project from The Hong Kong University of Science and Technology

• Big Data Technology Capstone Project from The Hong Kong University of Science and Technology

• Fondamentaux de la science des données from Université de Montréal

• Learning Time Series with Interventions from Massachusetts Institute of Technology

• Knowledge Inference and Structure Discovery for Education from University of Pennsylvania

• STAT115 from Harvard University

• Science des données et santé from Université de Montréal

• The impact of big data on healthcare from University of Cambridge

• Stanford Seminar - Theories of Inference for Visual Analysis from Stanford University

• Mining Online Data Across Social Networks from Stanford University

• Introduction to R and Geographic Information Systems (GIS) from Massachusetts Institute of Technology

• How to Analyze Research Data - Kristin Sainani from Stanford University

• Next in Data Visualization - Interactive Systems for Intelligence Augmentation from Harvard University

• Data Mining - The Tool of the Information Age from Stanford University

• Análisis de datos: Diseño y Visualización de Tableros from Delft University of Technology

• Stanford Seminar - Towards Theories of Single-Trial High Dimensional Neural Data Analysis from Stanford University

• Stanford Seminar - Big Data Is -At Least- Four Different Problems from Stanford University

• Stanford Seminar - TSAR - Anirudh Todi of Twitter from Stanford University

• Mathematics of Big Data and Machine Learning from Massachusetts Institute of Technology

• Data Analysis for Social Scientists from Massachusetts Institute of Technology

• Data Analysis for Social Scientists from Massachusetts Institute of Technology

• Stanford Seminar - Developing Design Spaces for Visualization - Tamara Munzner from Stanford University

• Stanford Seminar - Forecasting and Predicting the Future from Stanford University

• The End of Privacy from Stanford University

• Análisis de datos: Llévalo al MAX() from Delft University of Technology

• Jupyter Notebooks and Academic Publication from Stanford University

• Quantitative Biology Workshop from Massachusetts Institute of Technology

• Method of Detection - The Critical Missing Link in U.S. Cancer Registries from Yale University

• Stanford Seminar - Algorithmic Extremism- Examining YouTube's Rabbit Hole of Radicalization from Stanford University

• AI for Academic Search （人工智能助力学术搜索） from Tsinghua University

• Big Data's Big Deal - Viktor Mayer-Schonberger from University of Oxford

• Stanford Seminar - Mobilizing the Future from Stanford University

• Data for the People - Andreas Weigend of Social Data Lab from Stanford University

• Big Data, Big Impact - Preventing Drug Interactions, Understanding Behavior, and Climate Change from Stanford University

• Understanding the World Through Data from Massachusetts Institute of Technology

• 大数据技术与应用 from Tsinghua University

• Communicating Complex Statistical Ideas to the Public - Lessons from the Pandemic - D. Spiegelhalter from University of Oxford

• Grid Modernization and the Integration of Distributed Resources from Stanford University

• Stanford Seminar - Zhang Lin on Mobile Urban Sensing in Beijing from Stanford University

• Human-Machine Symbiosis in Data Visualization from Stanford University

• Harnessing Data for Social Impact - Empowering Communities through Visualization and Social Computing from Stanford University

• 大数据系统基础 from Tsinghua University

• 疫情防控，大数据分析大显身手 from Tsinghua University

• 清华大数据应用实践：快速搭建基于数据的应用 from Tsinghua University

• 数据与智能技术应用 from Tsinghua University

• 基于数据驱动的网络技术应用 from Tsinghua University

• 提升检索（Search）力就是提升研究（Research）力 from Tsinghua University

• 清华大学计算机科学与技术系六十周年系庆学术报告（四）大数据 from Tsinghua University

• “清华数为”大数据智能软件栈 from Tsinghua University

• 大数据可视化 from Zhejiang University

Information Security (InfoSec) (55)

• Web Security from Stanford University ★★★★★(87)

• Computer Systems Security from Massachusetts Institute of Technology ★★★★★(52)

• Web Security Fundamentals from KU Leuven University ★★★★☆(22)

• Engineering Cyber Resiliency - A Pragmatic Approach from Stanford University ★★★★★(22)

• Hash, Hack, Code - Emerging Trends in Cyber Security from Stanford University ★★★★★(12)

• CS50's Introduction to Cybersecurity from Harvard University ★★★★★(6)

• Intro to Information Security from Georgia Institute of Technology ★★☆☆☆(2)

• Network Security from Georgia Institute of Technology ★★★★★(1)

• Cyber Security Economics from Delft University of Technology ★★☆☆☆(1)

• Stanford Seminar - The Current State of Cybersecurity from Stanford University ★★★★★(1)

• Big Breaches - What We Learned From the World's Most Disruptive Cybersecurity Attacks from Stanford University ★★★★☆(1)

• Solving Cybersecurity as an Economic Problem from Stanford University ★★★★☆(1)

• Stanford Seminar - Computer Security- The Mess We're In, How We Got Here, and What to Do About It from Stanford University ★★★★★(1)

• Stanford Seminar - Preventing Successful Cyberattacks Using Strongly-Typed Actors from Stanford University ★★★★☆(1)

• Cyber-Physical Systems Security from Georgia Institute of Technology

• Mobile Payment Security from New York University (NYU)

• La cybersécurité en milieu universitaire from Université de Montréal

• Building Your Shield - Mapping the Cybersecurity Market from Stanford University

• Securing the World Around Us - Cyber Security for the Physical Economy from Stanford University

• The Growing Threat and Impact of Web-Based Malware - Stanford Computer Security from Stanford University

• Cybersecurity for Critical Urban Infrastructure from Massachusetts Institute of Technology

• Security Challenges in 5G Wireless and Beyond from New York University (NYU)

• Cybersecurity in Corporate Law: Key Issues for Practitioners and Policy-makers from New York University (NYU)

• Anatomy of an Attack - Understanding and Protecting Against Cyber Threats from New York University (NYU)

• Stanford Seminar - Online Political Ad Transparency from Stanford University

• Taking Memory Forensics to the Next Level from New York University (NYU)

• Tales from the Risks Forum from Stanford University

• Cybersecurity Career Insights - From Tandon Cyber Fellow to VP at U.S. Bank from New York University (NYU)

• Cyber Security: Building Compromise Resilient Systems and Key Management - Dr. Justin Cappos from New York University (NYU)

• Too Big to Fail: Cybersecurity Issues in Financial Services from New York University (NYU)

• angr: Binary Analysis Framework - Demonstration and Analysis from New York University (NYU)

• Stanford Webinar - To Attribute or Not Attribute, Is That the Question? from Stanford University

• Industry Insights: Cyber Security - Trends, Impacts, and Career Opportunities from University of Melbourne

• Cybersecurity: Keeping the Lights On - Practical Challenges in Utilities - Keynote from New York University (NYU)

• Locking the Web Open - A Call for a New, Decentralized Web from Stanford University

• Stanford Seminar - Thunderclap & CHERI - Capability Hardware-Enhanced RISC Instructions from Stanford University

• Bulletproofs - Short Proofs for Confidential Transactions and More from Stanford University

• How Can Privacy Exist in a Data-Driven World? - Stanford Seminar from Stanford University

• Privacy and the Power of Unknowing from Stanford University

• Hacking Reality: Technology for Detecting Deep Fakes from New York University (NYU)

• Firing Rounds at the Analysis Shooting Gallery - CSAW'16 Security Workshop from New York University (NYU)

• Lessons from Mirai and the Current State of IoT Security from Stanford University

• Exploiting Modern Microarchitectures - Meltdown, Spectre, & Other Hardware Attacks from Stanford University

• RowHammer, RowPress and Beyond: Can We Be Free of Bitflips Soon? - Stanford Seminar from Stanford University

• Proving Confidentiality and Its Preservation for Mixed-Sensitivity Concurrent Programs from University of Melbourne

• Protect Your Patients - Cybersecurity Essentials for Medical Software Course from Yale University

• Medical Software's Lifespan - Maintenance and Safe Retirement Strategy from Yale University

• 网络安全博弈之路 from Tsinghua University

• Trading Privacy for Convenience in the Age of Technology - Part 2 from New York University (NYU)

• IC Layout Security - Hardening Integrated Circuit Designs Against Adversaries from New York University (NYU)

• Empirical Measurement of Security and Privacy in Technology Systems - Conversations with Dr. Damon McCoy from New York University (NYU)

• Dispelling the Top 10 Myths in Cybersecurity - AIG Cybersecurity Lecture from New York University (NYU)

• Democracy Confronts Cyber Insecurity - Lecture 9 from New York University (NYU)

• Web安全实践 from University of Science and Technology of China

• 高级计算机网络 from University of Science and Technology of China

We did so by combining popular university rankings to identify the best institutions, and then using the Class Central database to find all their math online courses.

Methodology

I built the list following the same data-driven approach used to build the list of computer science courses from the top CS universities.

First, I identified the leading world university rankings. Since I was specifically interested in math, I looked at their latest rankings of the best universities for studying math (or closest superset). Here are the rankings I ended up using:

• QS: World University Ranking 2025 — Mathematics

• Times Higher Education: World University Ranking 2025 — Physical Sciences

Then, we crawled and scraped each ranking.

Now that I had some data, I used Jupyter with Python to process it. I combined the three rankings into one by averaging the position of each university in each ranking. Then, I filtered out the universities that didn’t offer online courses, and limited the list to the top 60 institutions.

For the latest update of this article, Suparn used the same sources and methodology to find the top 60 universities offering online math courses.

Combined ranking: top 10 universities for studying mathematics in 2024

As you can see in the image above, I found that the top three math institutions are:

1. Harvard

2. MIT

3. Stanford

Finally, we used the Class Central database, with its 250K online courses, to find all the math courses offered by the universities in the ranking.

The end result is a list of more than 300 online courses offered by 60 best universities in the world for studying math in 2025.

While processing the data, I noticed something interesting: 59 of the top 60 universities offer online courses, a lot more than I would have guessed. The world’s top institutions are very prolific creators of online courses.

Stats

• Enrollments range from 21 to over 13 million. There are 8 courses with over 1 million enrollments

• Altogether, the courses in this list have over 52 million enrollments, with an average of over 296 thousand enrollments

• 222 courses are free and 98 are paid

• 291 courses are in English, 19 Chinese, 7 French, and 1 each in Spanish, German, Korean.

• Together, they account for 2,118 reviews at Class Central, with an average of 28 reviews

• Average Rating 4.51 out of 5.0

• 80 courses are beginner level, 57 are intermediate level, and 11 are advanced level.

General Mathematics (45)

• Fibonacci Numbers and the Golden Ratio from The Hong Kong University of Science and Technology ★★★★★(271)

• How to Learn Math: For Students from Stanford University ★★★★☆(17)

• Convex Optimization from Stanford University ★★★★★(8)

• Mathematics for Engineers: The Capstone Course from The Hong Kong University of Science and Technology ★★★★★(5)

• A-level Mathematics for Year 12 - Course 1: Algebraic Methods, Graphs and Applied Mathematics Methods from Imperial College London ★★★★★(2)

• Mathematical Thinking in Computer Science from University of California, San Diego ★★★★★(2)

• Engineering Mathematics from University of Washington ★★★★★(1)

• Mathematics for Engineers from The Hong Kong University of Science and Technology ★★★★★(1)

• Political Geometry - The Mathematics of Redistricting from Harvard University ★★★★★(1)

• Essential Math for AI from Columbia University

• A-level Further Mathematics for Year 12 - Course 1: Complex Numbers, Matrices, Roots of Polynomial Equations and Vectors from Imperial College London

• Delivery Problem from University of California, San Diego

• A-level Further Mathematics for Year 13 - Course 1: Differential Equations, Further Integration, Curve Sketching, Complex Numbers, the Vector Product and Further Matrices from Imperial College London

• Cómo Aprender Matemáticas - Para Estudiantes from Stanford University

• Further Mathematics Year 13 course 2: Applications of Differential Equations, Momentum, Work, Energy & Power, The Poisson Distribution, The Central Limit Theorem, Chi Squared Tests, Type I and II Errors from Imperial College London

• Foundational Mathematics for AI from Johns Hopkins University

• Introduction to Complexity Science from Nanyang Technological University

• Introduction to optimization on smooth manifolds: first order methods from École Polytechnique Fédérale de Lausanne

• Nonlinear Dynamics and Chaos from Cornell University

• Mathematics of Big Data and Machine Learning, IAP 2020 from Massachusetts Institute of Technology

• How Learning Ten Equations Can Improve Your Life - David Sumpter from University of Oxford

• 离散数学 from Shanghai Jiao Tong University

• The Mathematics of Visual Illusions - Ian Stewart from University of Oxford

• Math Boot Camp for Engineers from Massachusetts Institute of Technology

• Wrinkling - Oxford Mathematics Research Seminar from University of Oxford

• Analyse I (partie 4) : Limite d'une fonction, fonctions continues from École Polytechnique Fédérale de Lausanne

• Analyse I (partie 5) : Fonctions continues et fonctions dérivables, la fonction dérivée from École Polytechnique Fédérale de Lausanne

• The Potential for AI in Science and Mathematics from University of Oxford

• Stanford Lecture - Pi and The Art of Computer Programming - 2019 from Stanford University

• Analyse I (partie 6) : Etudes des fonctions, développements limités from École Polytechnique Fédérale de Lausanne

• Prime Time - James Maynard from University of Oxford

• Bach and the Cosmos from University of Oxford

• Analyse I (partie 1) : Prélude, notions de base, les nombres réels from École Polytechnique Fédérale de Lausanne

• Analyse I (partie 7) : Intégrales indéfinies et définies, intégration (chapitres choisis) from École Polytechnique Fédérale de Lausanne

• The Number Mysteries - Marcus du Sautoy from University of Oxford

• Analyse I (partie 2) : Introduction aux nombres complexes from École Polytechnique Fédérale de Lausanne

• Analyse I (partie 3) : Suites de nombres réels I et II from École Polytechnique Fédérale de Lausanne

• Introduction to Metric Spaces from Massachusetts Institute of Technology

• Cascading Principles - Conrad Shawcross, Martin Bridson and James Sparks with Fatos Ustek from University of Oxford

• Why Does Rudolph Have a Shiny Nose? - Chris Budd from University of Oxford

• Blueprints - How Mathematics Shapes Creativity from University of Oxford

• Théorie des Groupes (partie 1) - Une introduction à la théorie des catégories from École Polytechnique Fédérale de Lausanne

• Productive Generalization - Timothy Gowers from University of Oxford

• Euler’s Pioneering Equation from University of Oxford

• The Travelling Santa Problem and Other Seasonal Challenges - Marcus du Sautoy from University of Oxford

Linear Algebra (32)

• Matrix Algebra for Engineers from The Hong Kong University of Science and Technology ★★★★★(843)

• Linear Algebra - Foundations to Frontiers from The University of Texas at Austin ★★★★☆(15)

• Introduction to Linear Models and Matrix Algebra from Harvard University ★★★★☆(12)

• Mathematics for Machine Learning: Linear Algebra from Imperial College London ★★★☆☆(9)

• Linear Algebra from Massachusetts Institute of Technology ★★★★★(5)

• Matrix Methods in Data Analysis, Signal Processing, and Machine Learning from Massachusetts Institute of Technology ★★★★★(3)

• Algèbre Linéaire (Partie 1) from École Polytechnique Fédérale de Lausanne ★★★★★(2)

• MIT - A 2020 Vision of Linear Algebra, Spring 2020 from Massachusetts Institute of Technology ★★★★★(2)

• Algèbre Linéaire (Partie 2) from École Polytechnique Fédérale de Lausanne ★★★★★(1)

• Algèbre Linéaire (Partie 3) from École Polytechnique Fédérale de Lausanne ★★★★★(1)

• Linear Algebra III: Determinants and Eigenvalues from Georgia Institute of Technology ★★★★★(1)

• Linear Algebra IV: Orthogonality & Symmetric Matrices and the SVD from Georgia Institute of Technology ★★★★★(1)

• Applications of Linear Algebra from Georgia Institute of Technology ★★★★★(1)

• Linear Algebra: Linear Systems and Matrix Equations from Johns Hopkins University

• Linear Algebra II: Matrix Algebra from Georgia Institute of Technology

• Linear Algebra: Matrix Algebra, Determinants, & Eigenvectors from Johns Hopkins University

• Linear Algebra I: Linear Equations from Georgia Institute of Technology

• Math for AI beginner part 1 Linear Algebra from Korea Advanced Institute of Science and Technology

• Introduction to Linear Algebra from The University of Sydney

• Linear Algebra: Orthogonality and Diagonalization from Johns Hopkins University

• Advanced Linear Algebra: Foundations to Frontiers from The University of Texas at Austin

• Engineering Mathematics: Linear Algebra and ODEs, Fall 2014 from Massachusetts Institute of Technology

• Linear Algebra from Elementary to Advanced from Johns Hopkins University

• Introductory Linear Algebra from Georgia Institute of Technology

• Linear Algebra from Massachusetts Institute of Technology

• Matrix Calculus for Machine Learning and Beyond from Massachusetts Institute of Technology

• A Vision of Linear Algebra from Massachusetts Institute of Technology

• Computational Science and Engineering I from Massachusetts Institute of Technology

• Linear Algebra and NxN Systems from Massachusetts Institute of Technology

• 线性代数(1) from Tsinghua University

• 线性代数(2) from Tsinghua University

• 简明线性代数 from Tsinghua University

Differential Equations (12)

• Differential Equations for Engineers from The Hong Kong University of Science and Technology ★★★★★(374)

• Differential Equations Part I Basic Theory from Korea Advanced Institute of Science and Technology ★★★★★(2)

• Differential Equations from Massachusetts Institute of Technology ★★★★★(2)

• Differential Equations Part II Series Solutions from Korea Advanced Institute of Science and Technology

• Differential Equations Part III Systems of Equations from Korea Advanced Institute of Science and Technology

• Engineering Math: Differential Equations and Linear Algebra from Massachusetts Institute of Technology

• Numerical Solution of Differential Equations - Oxford Mathematics 3rd Year Student Lecture from University of Oxford

• Differential Equations from Massachusetts Institute of Technology

• Differential Equations: Fourier Series and Partial Differential Equations from Massachusetts Institute of Technology

• Introduction to Differential Equations from Massachusetts Institute of Technology

• Differential Equations: 2x2 Systems from Massachusetts Institute of Technology

• A-level Further Mathematics for Year 13 - Course 1: Differential Equations, Further Integration, Curve Sketching, Complex Numbers, the Vector Product and Further Matrices from Imperial College London

Statistics & Probability (54)

• Intro to Statistics from Stanford University ★★★★☆(39)

• Probability - The Science of Uncertainty and Data from Massachusetts Institute of Technology ★★★★★(34)

• Statistical Learning with R from Stanford University ★★★★☆(28)

• Fundamentals of Statistics from Massachusetts Institute of Technology ★★★★☆(10)

• Data Analysis: Basic Probability and Statistics from Harvard University ★★★★☆(6)

• Introduction to Probability and Data with R from Duke University ★★★★☆(6)

• Statistical Inference and Modeling for High-throughput Experiments from Harvard University ★★★★★(4)

• Computational Probability and Inference from Massachusetts Institute of Technology ★★★★★(3)

• Introduction to Statistics from Stanford University ★★★★★(2)

• Statistics: Unlocking the World of Data from University of Edinburgh ★★★★☆(2)

• Introduction to Probability from Harvard University ★★★★★(1)

• Introduction to Statistics & Data Analysis in Public Health from Imperial College London ★★★★★(1)

• Summary Statistics in Public Health from Johns Hopkins University ★★★★★(1)

• Hypothesis Testing in Public Health from Johns Hopkins University ★★★★★(1)

• A Crash Course in Causality: Inferring Causal Effects from Observational Data from University of Pennsylvania ★★★★☆(1)

• Introductory Statistics : Basic Ideas and Instruments for Statistical Inference from Seoul National University ★☆☆☆☆(1)

• Statistics for Applications from Massachusetts Institute of Technology ★★★★★(1)

• Causal Inference from Columbia University

• Causal Inference 2 from Columbia University

• Introductory Statistics : Analyzing Data Using Graphs and Statistics from Seoul National University

• Survival Analysis in R for Public Health from Imperial College London

• Statistics for Data Science Essentials from University of Pennsylvania

• Logistic Regression in R for Public Health from Imperial College London

• Introduction to Probability Management from Stanford University

• Statistics Using Python from University of Wisconsin–Madison

• Probability and Statistics I: A Gentle Introduction to Probability from Georgia Institute of Technology

• Introductory Statistics : Sample Survey and Instruments for Statistical Inference from Seoul National University

• Probability and Statistics III: A Gentle Introduction to Statistics from Georgia Institute of Technology

• Inferential Statistics from Duke University

• Statistics 110 - Probability from Harvard University

• Mathematical understanding of uncertainty from Seoul National University

• Probability and Statistics II: Random Variables – Great Expectations to Bell Curves from Georgia Institute of Technology

• Probability and Statistics IV: Confidence Intervals and Hypothesis Tests from Georgia Institute of Technology

• What are the Chances? Probability and Uncertainty in Statistics from Johns Hopkins University

• Advanced Statistics for Data Science from Johns Hopkins University

• Advanced Probability and Statistical Methods from Johns Hopkins University

• Probabilistic Systems Analysis and Applied Probability from Massachusetts Institute of Technology

• Statistics with Python from University of Michigan

• Statistics with Python Using NumPy, Pandas, and SciPy from University of Michigan

• Probabilistic Graphical Models from Stanford University

• Probabilistic Systems Analysis and Applied Probability from Massachusetts Institute of Technology

• Selected Topics on Discrete Choice from École Polytechnique Fédérale de Lausanne

• Bayesian Networks 3 - Maximum Likelihood - Stanford CS221: AI (Autumn 2019) from Stanford University

• Bayesian Networks 1 - Inference - Stanford CS221: AI from Stanford University

• Bayesian Networks 2 - Forward-Backward - Stanford CS221: AI from Stanford University

• Statistics, Confidence Intervals and Hypothesis Tests from Georgia Institute of Technology

• MIT RES.6-012 Introduction to Probability, Spring 2018 from Massachusetts Institute of Technology

• Data Overload - Making Sense of Statistics in the News, Kristin Sainani from Stanford University

• Probability/Random Variables from Georgia Institute of Technology

• Dependent Stopping Times and Application to Credit Risk Theory - BQE Lecture Series from New York University (NYU)

• Inferenzstatistik from Duke University

• The Counting Project - How Data Built the Modern World from University of Oxford

• R을 사용한 확률 및 데이터 소개 from Duke University

• Statistical Methods for Computer Science from Johns Hopkins University

Calculus (30)

• Calculus: Single Variable Part 1 - Functions from University of Pennsylvania ★★★★★(8)

• Introduction to Calculus from The University of Sydney ★★★★★(6)

• Calculus: Single Variable Part 2 - Differentiation from University of Pennsylvania ★★★★★(5)

• Calculus for Engineers from The Hong Kong University of Science and Technology ★★★★★(5)

• Calculus: Single Variable Part 3 - Integration from University of Pennsylvania ★★★★☆(4)

• Calculus: Single Variable Part 4 - Applications from University of Pennsylvania ★★★★★(3)

• Single Variable Calculus from Massachusetts Institute of Technology ★★★★★(3)

• Calculus Applied! from Harvard University ★★★★★(2)

• Combinatorial Mathematics | 组合数学 from Tsinghua University ★★★★☆(2)

• Introductory Calculus - Oxford Mathematics 1st Year Student Lecture from University of Oxford ★★★★★(2)

• Calculus through Data & Modeling: Differentiation Rules from Johns Hopkins University ★★★★☆(1)

• Single Variable Calculus from University of Pennsylvania

• Applied Calculus with Python from Johns Hopkins University

• Calculus through Data & Modeling: Precalculus Review from Johns Hopkins University

• Applied Math for Materials Science and Engineering from Korea Advanced Institute of Science and Technology

• Calculus through Data & Modeling: Limits & Derivatives from Johns Hopkins University

• A-Level Further Mathematics for Year 12 - Course 2: 3 x 3 Matrices, Mathematical Induction, Calculus Methods and Applications, Maclaurin Series, Complex Numbers and Polar Coordinates from Imperial College London

• Calculus through Data & Modelling: Series and Integration from Johns Hopkins University

• Calculus through Data & Modelling: Techniques of Integration from Johns Hopkins University

• Calculus through Data & Modelling: Integration Applications from Johns Hopkins University

• Calculus through Data & Modeling: Applying Differentiation from Johns Hopkins University

• Introduction to Advanced Calculus from The University of Sydney

• Calculus 1C: Coordinate Systems & Infinite Series from Massachusetts Institute of Technology

• Calculus 1A: Differentiation from Massachusetts Institute of Technology

• Calculus 1B: Integration from Massachusetts Institute of Technology

• 微积分（先修课） from Tsinghua University

• A-Level Further Mathematics for Year 12 - Course 2: 3 x 3 Matrices, Mathematical Induction, Calculus Methods and Applications, Maclaurin Series, Complex Numbers and Polar Coordinates from Imperial College London

• 微积分——极限理论与一元函数 from Tsinghua University

• 微积分-1 from Tsinghua University

• 微积分-2 from Tsinghua University

Mathematical Modeling (9)

• Modelling Ice Sheets - Oxford Mathematics Research Seminar from University of Oxford ★★★★★(4)

• Infectious Disease Modeling in Practice from Johns Hopkins University

• Scaling the Maths of Life from University of Oxford

• How Do Mathematicians Model Infectious Disease Outbreaks? from University of Oxford

• Mathematical Modelling in Biology - Neuronal Signalling from University of Oxford

• Mathematical Modelling in Biology - Enzyme Kinetics and Perturbation Theory - Lecture 2 from University of Oxford

• Responsible Modelling and the Ethics of Mathematics for Decision Support from University of Oxford

• 3 Minute Thesis Competition 2025 - Oxford Mathematics Postgraduate Research Presentations from University of Oxford

• Going for Gold - The Mathematics of Sporting Glory from University of Oxford

Convex Optimization (17)

• Convex Optimization I - Lecture 1 from Stanford University

• Convex Optimization I - Lecture 3 from Stanford University

• Convex Optimization I - Lecture 2 from Stanford University

• Convex Optimization I - Lecture 8 from Stanford University

• Convex Optimization I - Lecture 5 from Stanford University

• Convex Optimization I - Lecture 4 from Stanford University

• Convex Optimization I - Lecture 6 from Stanford University

• Convex Optimization I - Lecture 7 from Stanford University

• Convex Optimization I - Lecture 13 from Stanford University

• Convex Optimization I - Lecture 16 from Stanford University

• Convex Optimization I - Lecture 15 from Stanford University

• Convex Optimization I - Lecture 10 from Stanford University

• Convex Optimization I - Lecture 9 from Stanford University

• Convex Optimization I - Lecture 12 from Stanford University

• Convex Optimization I - Lecture 11 from Stanford University

• Convex Optimization I - Lecture 17 from Stanford University

• 最优化理论与方法 from Nanjing University

Probability Theory (8)

• 概率论与数理统计 from Tsinghua University

• Introduction to Probability from Massachusetts Institute of Technology

• How to Put Uncertainty into Numbers - Understanding Chance, Luck and Ignorance from University of Oxford

• Can We Truly Understand by Counting? Understanding Complex Physical Systems Through Mathematics from University of Oxford

• Probability, Measure and Martingales - Let There Be Time: Filtrations and Stopping Times from University of Oxford

• Martingales: Definition and First Properties - Probability, Measure and Martingales from University of Oxford

• Probability, Measure and Martingales - Stopped Martingales and Optional Sampling Theorems from University of Oxford

• Vitali's Convergence Theorem and Martingale Inequalities - Lecture 5 from University of Oxford

Others (113)

• Vector Calculus for Engineers from The Hong Kong University of Science and Technology ★★★★★(261)

• Introduction to Mathematical Thinking from Stanford University ★★★★☆(51)

• Bayesian Statistics from Duke University ★★★☆☆(12)

• Discovery Precalculus: A Creative and Connected Approach from The University of Texas at Austin ★★★★★(3)

• Combinatorics and Probability from University of California, San Diego ★★★★☆(3)

• Analytic Combinatorics from Princeton University ★★★★☆(3)

• Data Science: Linear Regression from Harvard University ★★☆☆☆(3)

• Engineering Calculus and Differential Equations from The University of Hong Kong ★★★★★(2)

• Multivariable Calculus from Massachusetts Institute of Technology ★★★★★(2)

• Introduction to Graph Theory from University of California, San Diego ★★★★★(2)

• Linear Regression and Modeling from Duke University ★★★★☆(2)

• Linear Regression in R for Public Health from Imperial College London ★★★★★(1)

• Simple Regression Analysis in Public Health from Johns Hopkins University ★★★★★(1)

• Precalculus from Modern States ★★★★★(1)

• Algebra: Elementary to Advanced - Functions & Applications from Johns Hopkins University ★★★★☆(1)

• Algebra: Elementary to Advanced from Johns Hopkins University ★★★★★(1)

• Cours préparatoire: Fonctions Trigonométriques, Logarithmiques et Exponentielles from École Polytechnique Fédérale de Lausanne ★★★★☆(1)

• The Banach Contraction Mapping Theorem - Oxford Mathematics 1st Year Lecture from University of Oxford ★★★★☆(1)

• The Seduction of Curves - The Lines of Beauty That Connect Mathematics, Art and The Nude from University of Oxford ★★★★★(1)

• Hamiltonian Paths in Antiquity - Stanford Lecture 2016 from Stanford University ★★★★★(1)

• Precalculus: Relations and Functions from Johns Hopkins University

• Precalculus: Mathematical Modeling from Johns Hopkins University

• Precalculus: Periodic Functions from Johns Hopkins University

• Precalculus through Data and Modelling from Johns Hopkins University

• Combinatorics and Algorithms Design from Tsinghua University

• 组合数学 from Tsinghua University

• Linear Regression Modeling for Health Data from University of Michigan

• Differential Calculus through Data and Modeling from Johns Hopkins University

• Differential Calculus from Massachusetts Institute of Technology

• Graph Theory and Additive Combinatorics from Massachusetts Institute of Technology

• Graph Theory and Additive Combinatorics from Massachusetts Institute of Technology

• Petri网：模型、理论与应用 from Tsinghua University

• Multivariable Calculus from Massachusetts Institute of Technology

• Multivariable Calculus 1: Vectors and Derivatives from Massachusetts Institute of Technology

• Multidimensional Analysis and Geometry - Introduction to the Derivative in Higher Dimensions - Lecture 1 from University of Oxford

• Multivariable Calculus 3: Theorems and Applications from Massachusetts Institute of Technology

• Multivariable Calculus 2: Integrals from Massachusetts Institute of Technology

• 微积分——多元函数与重积分 from Tsinghua University

• 多元微积分（先修课） from University of Science and Technology of China

• Using Language Models to Understand Wage Premia from Stanford University

• Algebra: Elementary to Advanced - Equations & Inequalities from Johns Hopkins University

• Algebra: Elementary to Advanced - Polynomials and Roots from Johns Hopkins University

• Honors Algebra 2: Linear and Quadratic Functions from Johns Hopkins University

• History of Mathematics - Classical Algebra - 19th-Century Beginnings of Modern Algebra - 3rd Year Lecture from University of Oxford

• History of Mathematics - Classical Algebra: Equation Solving 1800 BC - AD 1800 - 3rd Year Lecture from University of Oxford

• Honors Algebra 2: Algebraic, Exponential & Log Functions from Johns Hopkins University

• Honors Algebra 2 from Johns Hopkins University

• Honors Algebra 2 from Johns Hopkins University

• Honors Algebra 2: Series, Trigonometry, and Probability from Johns Hopkins University

• Geometry - Scalar Triple Product: Oxford Mathematics First Year Student Lecture from University of Oxford

• Introduction to Conic Sections and Their Mathematical Properties - Lecture 1 from University of Oxford

• Natural Tilings: From Hard Rock to Soft Cells from University of Oxford

• Calculus through Data & Modelling: Vector Calculus from Johns Hopkins University

• Math for AI Beginner Part 2 : Vector Calulus from Korea Advanced Institute of Science and Technology

• Multidimensional Analysis and Geometry: Introduction to the Derivative in Higher Dimensions - Lecture 2 from University of Oxford

• Real Analysis from Massachusetts Institute of Technology

• Real Analysis from Massachusetts Institute of Technology

• Discrete Mathematics from Shanghai Jiao Tong University

• 离散数学概论 Discrete Mathematics Generality from Peking University

• Mathematics for Computer Science from Massachusetts Institute of Technology

• Data Science Decisions in Time: Using Causal Information from Johns Hopkins University

• 教育定量研究方法（高级） from Tsinghua University

• Optimization: principles and algorithms - Linear optimization from École Polytechnique Fédérale de Lausanne

• 运筹学 from Tsinghua University

• Meaningful Predictive Modeling from University of California, San Diego

• Foundations of Probability and Random Variables from Johns Hopkins University

• Stanford CS109 - Future of Probability - Lecture 28 from Stanford University

• Probability, Measure and Martingales: An Introduction from University of Oxford

• From Coin Flips to Clinical Trials - Introduction to Probability - Medical Software Course from Yale University

• Introduction to Uncertainty Quantification from Johns Hopkins University

• Random Processes from Johns Hopkins University

• Algebraic Curves - Curves in Affine Plane and the Projective Plane - Lecture 3 from University of Oxford

• Algebraic Curves - More on Projective Geometry - Lecture 2 from University of Oxford

• Algebraic Curves - Properties of Algebraic Curves - Lecture 4 from University of Oxford

• Numerical Methods Applied to Chemical Engineering from Massachusetts Institute of Technology

• The Butterfly Effect - What Does It Really Signify? from University of Oxford

• Integral Calculus through Data and Modeling from Johns Hopkins University

• Foundations of Logic from Tsinghua University

• Paradox and Infinity from Massachusetts Institute of Technology

• Single Variable Calculus from Massachusetts Institute of Technology

• Introduction to Functional Analysis from Massachusetts Institute of Technology

• Groups and Group Actions - Representations of Groups by Permutations from University of Oxford

• Théorie des Groupes (partie 2) - Quotients de groupe from École Polytechnique Fédérale de Lausanne

• Théorie des Groupes (partie 3) - Actions de groupe from École Polytechnique Fédérale de Lausanne

• Théorie des Groupes (partie 4) - Groupes abéliens et sous-groupes de Sylow from École Polytechnique Fédérale de Lausanne

• Groups and Group Actions - Group Homomorphisms from University of Oxford

• How to Be a Statistical Detective from Stanford University

• Principles of Digital Communication II from Massachusetts Institute of Technology

• Knotty Problems - Marc Lackenby from University of Oxford

• Can Yule Solve My Problems - Alex Bellos from University of Oxford

• Analysis III: Basic Properties of Riemann Integration - Lecture 1 from University of Oxford

• Mathematical Methods for Engineers II from Massachusetts Institute of Technology

• Why There Are No Three-Headed Monsters from University of Oxford

• From Ronald Ross to ChatGPT: The Birth and Strange Life of the Random Walk from University of Oxford

• Introduction to Complex Numbers - Lecture 1 from University of Oxford

• A-level Further Mathematics for Year 12 - Course 1: Complex Numbers, Matrices, Roots of Polynomial Equations and Vectors from Imperial College London

• Geometry: Conics - Lecture 2 from University of Oxford

• Numbers Are Serious but They Are Also Fun - Michael Atiyah from University of Oxford

• Linear Diophantine Equations and the Extended Euclidean Algorithm - Lecture 1 from University of Oxford

• 1 + 1 = 2 - The Foundation of Mathematical Counting and Number Systems from University of Oxford

• Complex Analysis: Multivalued Functions and Integration - Lecture 4 from University of Oxford

• Algebraic Topology - Algebraic Invariants of Spaces from University of Oxford

• Algebraic Topology - Chains, Cycles, and Homology Classes from University of Oxford

• Honors Algebra 2: Polynomials and Complex Numbers from Johns Hopkins University

• Transfer Functions and the Laplace Transform from Massachusetts Institute of Technology

• The Taylor Series, Complex Numbers, and Simple Harmonic Motion - Lecture 16 from Yale University

• Social Sequence Analysis: An Introduction and Overview from The University of Chicago

• 数值分析与算法 from Tsinghua University

• Lie Groups - Introduction to Lie Groups from University of Oxford

• Lie Groups - The Exceptional Lie Group G2 from University of Oxford

• Algebraic Curves - Introduction to Projective Geometry - Lecture 1 from University of Oxford

• Commutative Algebra - Primary Decomposition from University of Oxford

• Commutative Algebra - Primary Decomposition 2 from University of Oxford

More Courses

With over 300 courses to pick from, we hope you find something you like. But if these aren’t enough, check out Class Central’s catalog of 250K online courses or our thematic collections:

• 1000+ Free Computer Science Courses from World’s Top Universities

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• 10,000+ Free Courses from Tech Giants: Learn from Google, Microsoft, Amazon, and More

• 8000+ OpenCourseWare Courses from Top Institutions.

But if you want to jump straight to the results, here are my picks:

1. Machine Learning (Stanford University)
2. Machine Learning Foundations: A Case Study Approach (University of Washington)
3. Machine Learning for All (University of London)
4. Machine Learning with Python (IBM)
5. Machine Learning (Georgia Tech)
6. Machine Learning Crash Course with TensorFlow APIs (Google)
7. Machine Learning A-Z: Hands-On Python & R In Data Science (Udemy)
8. Introduction to Machine Learning in Production (DeepLearning.AI)
9. Python for Data Science and Machine Learning Bootcamp (Udemy)
10. Machine Learning for Musicians and Artists (Goldsmith)

What is Machine Learning?

Machine learning is a subfield of artificial intelligence dedicated to the design of algorithms capable of learning from data. It has numerous applications, including business analytics, health informatics, financial forecasting, and self-driving cars.

In 2022, machine learning skills are widely in-demand. On Microsoft’s career page, 21% of the open developer positions currently mention “machine learning”. On Amazon’s career page, it’s 63%.

According to the Future of Jobs Report published by the World Economic Forum, machine learning is expected to be one of the world’s most in-demand skills through 2025.

Course Ranking Methodology

To create this ranking, I followed a three-step process:

First, I’m a developer at Class Central, the leading search engine for online courses.

So I went through our catalog of over 50K courses to put together a preliminary selection. I did so by taking into account factors like reviews, ratings, enrollments, bookmarks, and more.

So this was a rather objective step: I narrowed down the options by looking at well-defined metrics.

Second, I used my experience as an online learner to evaluate each preliminary pick.

Metrics such as course ratings rarely tell the whole story. I’ve completed many MOOCs, earned an online bachelor’s in computer science, and I’m enrolled in Georgia Tech’s online master’s in computer science (OMSCS). This has given me some perspective on what to look for in an online course, which I used to evaluate each of my preliminary picks.

So this was a rather subjective step: I combed through my picks to arrive at a near-final selection.

Third, I expanded this selection to include other valuable resources I’ve come across.

Since there are long-established courses in most topics, more recent courses on the same topic can go unnoticed. But sometimes, these are great. I made sure to include those when possible.

So this is a rather subjective step again: I rounded up my picks with excellent but less well-known courses.

The end result is a unique selection of courses that combines a decade of Class Central data and my own experience as an online learner to try to get the best of both worlds. So far, I’ve spent more than 15 hours building this list, and I’ll continue to update it.

Course Ranking Statistics

Here are some statistics regarding this course ranking:

• Combined, these courses have accrued over 6.6M enrollments.
• The most-represented course provider in the ranking is Coursera, with four courses.
• Combined, these courses have been bookmarked over 118,000 times on Class Central, while the machine learning subject itself has been bookmarked over 195,000 times.
• The most-popular course in the ranking has over 4M enrollments by itself.
• Eight courses are free or free-to-audit, while two are paid.
• Combined, these courses are received over 500 reviews on Class Central.

Without further ado, here are my picks for the best machine learning online courses.

1. Machine Learning (Stanford University)

Prof. Andrew Ng, instructor of the course

My first pick for best machine learning online course is the aptly named Machine Learning, offered by Stanford University on Coursera.

This is the seminal machine learning course, and a very special course indeed. Taught by Andrew Ng, it was one of the original courses that kicked-off the popularization of massive open online courses (MOOCs).

Bolstered by the course's success, Andrew Ng went on to cofound Coursera.

What You'll Learn

This course starts by laying down the mathematical foundations of machine learning. It begins with a review of linear algebra and univariate linear regression before moving on to multivariate and logistic regression.

It then jumps from topic to topic each week to cover a wide variety of machine learning techniques and models. These include deep learning, support-vector machines, and principal component analysis.

Finally, it touches on practical aspects such as how to design and leverage large-scale machine learning projects.

By the end of the course, you’ll have a broad understanding of machine learning, its concepts, and its methods. You’ll be able to implement fundamental machine learning algorithms such as back propagation and k-means clustering.

You’ll be equipped to tackle tasks such as multi-class classification and anomaly detection. And you’ll be able to use Octave and Matlab to complete practical projects involving optical character recognition using a wide variety of approaches.

One Thing to Note

This course uses Octave rather than, say, Python. The course will teach you the concepts but not the tools most commonly used nowadays in machine learning. Despite that, it remains in my view the machine learning course of choice, hence its top spot.

But if you’re looking for a course more relevant to the day-to-day of a machine learning practitioner, check the next pick.

How You’ll Learn

The course is broken down into 11 weeks. Each week involves about 6 hours of work. Concepts are taught through a combination of video lectures and readings.

In terms of assessments, each week includes at least one auto-graded quiz. Most include several. And most weeks include a multiple-hours long programming project. There are 8 in total.

Fun Facts

• The course launched in October 2011 with over 100K learners, just two months after being announced. It was unprecedented for a course to have this many learners.
• With 4.5M learners, it has grown to become one of the most popular online courses ever, if not the most popular ever.
• Besides being a professor of computer science at Stanford University, Andrew Ng was the former Chief Scientist at Baidu and the cofounder of Google Brain.
• The course has amassed over 68,000 bookmarks on Class Central.
• While the course includes a paid certificate, the entirety of the course material, including all the assignments, can be accessed for free.
• The instant popularity of the course encouraged Andrew Ng to found Coursera and, more recently, DeepLearning.AI.

If you're interested in this course, you can find more information about the course and how to enroll here.

2. Machine Learning Foundations: A Case Study Approach (University of Washington)

Prof. Emily Fox, co-instructor of the course

My second pick for the best machine learning online course is Machine Learning Foundations: A Case Study Approach, offered by the University of Washington on Coursera.

Many academic machine learning courses like to approach the subject from a rather abstract perspective. They spend a lot of time laying down mathematical foundations and relegating more tangible aspects of the discipline to examples and exercises. This course flips that script on its head.

As its name indicates, the course approaches machine learning through case studies, each with a well-defined context and objective. These case studies help ground machine learning concepts in reality.

Instead of learning how to do regressions just because, you’ll learn how to predict house prices with regressions. That doesn’t mean that the course glosses over the theoretical details. It just approaches the subject more pragmatically.

What You'll Learn

The course starts by contextualizing machine learning: explaining what machine learning is, going over some of its applications, and making a case for its importance in the future.

The course introduction also takes the time to cover Python fundamentals as well as the rudiments of tools like Jupyter Notebooks.

The course then moves from case study to case study, using each one to illustrate a particular facet of machine learning: you use regressions to predict house prices, you use classification to evaluate sentiments in user reviews, you use clustering for grouping related articles, you use deep learning to identify objects in images, and so on.

If you’re someone that likes to learn through examples, the clear mapping between tasks and concepts in this course might help make the subject more palatable to you.

By the end of this course, you’ll understand fundamental machine learning tasks like regression, classification, and clustering, and you’ll know when to use each technique.

You’ll know how to extract features from data and use this as inputs for your models. You’ll be able to evaluate your model's correctness using well-defined error metrics. And you’ll be able to implement machine learning applications end-to-end in Python.

How You’ll Learn

This course is broken down into 6 weeks. Each week involves about 3 hours of work. The course is taught through a mix of short videos and readings.

Regarding assessments, most weeks include two exercises that should take about an hour to complete.

One Thing to Note

When the course was released, it used GraphLab, an open-source machine learning tool started by Prof. Carlos Guestrin, one of the course co-instructors. Since then, GraphLab has become Turi, and the course now uses TuriCreate for the exercises.

The videos, however, still use GraphLab, and while both tools are similar, this has caused friction for some learners. So if you take the course, be prepared to do some Googling.

Fun Facts

• This course is the first of the four-part Machine Learning Specialization on Coursera.
• Emily Fox, who released the course while a Professor at the University of Washington, has since joined the Department of Statistics of Stanford University.
• Turi, the company behind the software you'll use in this course, that was started by the course co-instructor, Carlos Guestrin, was acquired by Apple in 2016 for $200M.

If you're interested in this course, you can find more information about the course and how to enroll here.

3. Machine Learning for All (University of London)

Prof. Marco Gillies, instructor of the course

My third pick for the best machine learning online course is Machine Learning for All, offered by the University of London on Coursera.

While most other courses either assume prior programming knowledge or teach you programming basics, this course aims to make machine learning accessible to a wider audience. It doesn't require advanced mathematical knowledge or the use of programming languages or machine learning libraries like Python and TensorFlow.

What You’ll Learn

This course starts by explaining what artificial intelligence and machine learning are and how these disciplines are connected.

It discusses various real-world applications of machine learning, including AlphaGo, a machine learning program capable of beating the best Go players in the world. It explains data representation, how to set up a machine learning project, and some of the opportunities and ethical considerations of machine learning.

Finally, the course invites you to implement a machine learning project by collecting data, training a model, and putting it to the test.

By the end of the course, you’ll be equipped with a broad understanding of machine learning, its various uses, and its significance for the future.

You’ll be be familiar with the most important technical concepts that underpin machine learning. You’ll have a high-level grasp of the process of building a machine learning model, from data collection to model evaluation.

And you’ll be prepared to tackle more advanced, theoretical courses on machine learning.

How You’ll Learn

The course is broken down into 4 weeks. Each week involves about 6 hours of work. The course is taught through a mix of video lectures and readings.

Regarding assessments, in most weeks, you’ll complete an hour-long autograded quiz, and in some weeks, you’ll also complete additional practical exercises.

Fun Facts

• Dr. Marco Gillies, the course instructor, is also a teacher in the University of London’s online bachelor’s degree in computer science, offered on Coursera.
• This course is the most approachable academic course in the ranking: it’s a bona fide university course, but it's tuned to be suitable to a broad audience.

If you're interested in this course, you can find more information about the course and how to enroll here.

4. Machine Learning with Python (IBM)

Explaining k-means clustering

This course offered by IBM on Coursera teaches machine learning through a hands-on approach using Python, which is nowadays the de facto programming language of artificial intelligence.

Beware, this course will throw math at you. If your calculus is rusty, you might want to brush up on that before taking this course.

What You’ll Learn

The course starts by covering machine learning fundamentals and applications in fields such as healthcare, banking, and telecommunications. And it explains the difference between supervised and unsupervised learning, and goes over which type of learning is suitable for which type of task.

Each week is dedicated to one of the broad machine learning tasks — regression, clustering, and classification — and the various methods that can be used to implement them, such as decision trees, support vector machines, and k-means.

By the end of the course, you’ll have covered a lot of ground in terms of the mathematical underpinnings of machine learning. You’ll be familiar with a large number of applications of machine learning in fields ranging from healthcare to high-performance computing.

You’ll be able to implement a tapestry of machine learning algorithms using Python. And you’ll have practiced using machine learning libraries such as scikit-learn and SciPy.

How You’ll Learn

This course is broken down into 4 weeks. Each week involves about 4 hours of work. The course is taught largely through video lessons.

Regarding assessments, each week culminates in a 10-minute practice exercise.

Fun Facts

• Saeed Aghabozorgi, one of the course co-instructors is a prolific researcher with over 3300 citations on Google Scholar.
• Besides this course, IBM offers a full-fledged Machine Learning Professional Certificate on Coursera which includes six courses.

If you're interested in this course, you can find more information on the course and how to enroll here.

5. Machine Learning (Georgia Tech)

Prof. Michael Littman and Prof. Charles Isbell, co-instructors of the course

This course is offered by the Georgia Institute of Technology on Udacity, and it’s also offered as part of Georgia Tech’s Online Master of Computer Science (OMSCS).

This course covers machine learning broadly, emphasizing breadth over depth. It favors a high-level approach of machine learning concepts rather than delving into the nitty gritty details of how to implement specific machine learning algorithms.

I’d argue that this course's main strength is its instructional approach.

The course is taught by two instructors, and the lessons are presented as a conversation between them, with one of the instructors playing the role of the student and raising questions.

The exchange is full of humor, which isn’t something that can be said of many machine learning courses.

What You’ll Learn

This course is divided into three broad machine learning tasks.

First, it covers supervised learning, discussing decision trees, regression and classification, and neural networks. Then, it covers unsupervised learning, discussing clustering, feature selection, and randomized optimization. Finally, it covers reinforcement learning, discussing markov decision processes, game theory, and decision making.

By the end of the course, you’ll have a comprehensive understanding of supervised, unsupervised, and reinforcement learning, and the differences between them.

You’ll learn methods tailored to each of these problems. And you’ll be able to implement methods to solve them, interpret the results of these methods, and evaluate their correctness.

How You’ll Learn

The course is broken down into 21 lessons, and each lesson is made up of short videos with in-video quizzes.

The course doesn’t include publicly available projects. Those can only be accessible by students taking the for-credit version of the course via Georgia Tech.

Fun Facts

• Charles Isbell and Michael Littman, the course instructors, recorded a machine learning version of Michael Jackson’s Thriller. It's very catchy.
• If you like the conversational approach of this course, these instructors have another similar course on reinforcement learning.

If you're interested in this course, you can find more information about the course and how to enroll here.

6. Machine Learning Crash Course with TensorFlow APIs (Google)

Dr. D. Sculley, co-instructor of the course

This course is offered by Google on their developer platform. While most of the courses in this ranking are academic in nature and rather long, this one fits squarely into the category of hands-on introductions to machine learning.

It’s also pragmatic and flexible in that, while it will invite a complete beginner to take the course in full, it will allow those that already have experience in machine learning to instead use the course as a refresher. And that idea is built into the course design, right from the start.

What You’ll Learn

The crash course begins by asking you about your background in machine learning. Depending on your answer, it will orient you toward the appropriate resources, so you can make the best use of your time.

Assuming you’re a complete beginner, you’ll start from square one. So your learning path will cover fundamental machine learning concepts, including regressions, loss functions, and gradient descent.

The course uses TensorFlow, Google’s popular machine learning library. So rapidly the low-level details will be abstracted away by leveraging the library functions.

Some learners could see this as a negative, since you can get away with not understanding how it all works under the hood. But if you're interested in quickly applying machine learning, this crash course should be right up your alley.

A thing to note is that the course also introduces neural networks, a topic many other short machine learning courses prefer to skip or barely touch, since it's a topic worthy of its own separate course.

Google’s crash course, however, is condensed enough to comfortably fit neural nets. But remember, it abstracts away lots of details, so if what you’re after is deep comprehension, you might be better served by another course.

How You’ll Learn

The crash course is broken down into three large sections: (1) machine learning concepts, (2) machine learning engineering, and (3) machine learning systems in the real world.

Each section consists of video lectures presented by Google researchers, readings, and quizzes for self-assessment.

Fun Facts

• The crash course has a series of follow-up mini-courses covering topics such as how to frame machine learning problems and how to debug machine learning pipelines.
• D. Scully, the course instructor, is Director of Engineering at Google Brain, which coincidentally was cofounded by Andrew Ng, the instructor of this ranking's top pick.

If you're interested in this course, you can find more information about the course and how to enroll here.

7. Machine Learning A-Z: Hands-On Python & R in Data Science

Kirill Eremenko, co-instructor of the course

True to its name, this Udemy course is a comprehensive but practical introduction to machine learning. It slowly works its way up from data preprocessing to model validation, but glosses over some of the underlying math.

If you want to jump straight into “doing”, this course might be a good fit.

What You’ll Learn

The course starts by covering various types of regression, classification, and clustering models. It discusses reinforcement learning as well as natural language processing, and it covers the fundamentals of artificial neural networks.

The course uses the Python and R programming languages, and the TensorFlow machine learning library.

How You’ll Learn

The course includes over 40 hours of video lessons, interspersed with practical exercises. You’ll build an intuition for each concept and method before applying them to solve concrete problems using dedicated machine learning libraries.

Fun Facts

• With over 800K registered learners, Machine Learning A-Z is the most popular machine learning courses on Udemy, and one of the most successful courses on the entire platform.
• Between them, the instructors Kirill Eremenko and Hadelin de Ponteves have created over 80 courses and have close to 3.5M students.

If you're interested in this course, you can find more information about the course and how to enroll here.

8. Introduction to Machine Learning in Production (DeepLearning.AI)

Prof. Andrew Ng, co-instructor of the course

After launching the machine learning course that tops this ranking and co-founding Coursera, Andrew Ng went on to create another company, DeepLearning.AI.

The company offers a wide variety of courses on machine learning and artificial intelligence, including this course, which covers how to use machine learning in a production environment.

Unlike previous courses, which mainly targeted a general audience (albeit not alway beginners), this course is geared toward learners who already have a solid understanding of machine learning. It targets students who would like to be able to confidently implement end-to-end machine learning pipelines in a professional setting.

What You’ll Learn

This course starts by discussing the lifecycle of a machine learning project and how to deploy production-ready machine learning systems.

Then, the course explains strategies to pick adequate models and train them, as well as some of the pitfalls to avoid when dealing with skewed data sets.

Finally, the course covers how to handle classification problems and how to establish a baseline to assess your model's performance.

How You’ll Learn

This course is broken down into three weeks. Each week involves about 3 hours of work. But remember, it’s an advanced course, so it assumes you already have a machine learning background. Otherwise, the workload could be much higher.

And much like Andrew Ng’s other courses, the course consists of video lessons and readings. Each week ends with several practical exercises using Python and specialized frameworks and libraries like PyTorch and Keras.

Fun Facts

• This course is the first of a four-course Coursera Specialization dedicated to MLOps, machine learning engineering for production.
• DeepLearning.AI offers many courses on Coursera, ranging from AI fundamentals to specialized deep learning topics such as generative adversarial networks.

If you're interested in this course, you can find more information about the course and how to enroll here.

9. Python for Data Science and Machine Learning Bootcamp

Jose Portilla, instructor of the course

As the de facto language of machine learning and AI (at least for now), Python is often a prerequisite of machine learning courses.

Some courses start with a Python refresher before jumping into actual machine learning. But if you’re a novice programmer, a simple refresher may not cut it.

So you either find a Python course and take that first, or you find a course that teaches both machine learning and Python. This is that course.

What You’ll Learn

In the first part of the course, after setting up your development environment, you’ll jump into a Python crash course. You'll learn the fundamentals of the programming language as well as a plethora of widely used libraries, such as NumPy, Pandas, and Matplotlib.

Once you've integrated these skills, you’ll be equipped to tackle the second part of the course, which is entirely dedicated to machine learning.

As usual, you’ll start with regression and work your way up from there, exploring machine learning models and systems ranging from k-means clustering to artificial neural networks.

How You’ll Learn

This course includes over 25 hours of video lessons interspersed with practical exercises. In addition, the course includes numerous references to external material, for those that want to go above and beyond.

Fun Facts

• With over 2.6M students on Udemy, the course instructor Jose Portilla is one of Udemy’s most popular instructors.
• While the course covers both Python and Machine learning, if you want to dive deeper into either subjects, Jose Portilla also teaches courses exclusively dedicated to Python and exclusively dedicated to machine learning.

If you're interested in this course, you can find more information about the course and how to enroll here.

10. Machine Learning for Musicians and Artists (Goldsmith)

Dr. Rebecca Fiebrink, instructor of the course

Finally, to end this ranking on a high note, my tenth pick is Machine learning for Musicians and Artists, offered by Goldsmith, University of London, through Kadenze.

This course is a bit unconventional: it approaches machine learning it from an artistic angle, from music to visual arts. If that’s actually the sort of mix you’re looking for, then consider this our one and only first pick.

What You’ll Learn

In this course, you’ll learn the fundamentals of machine learning, but you’ll do so by connecting the topic to art, motion, and sound. More specifically, you’ll learn how to use machine learning to interpret human movement, music, and other sources of real-time data.

Don’t worry, this course will also entail learning more pedestrian but essential machine learning concepts, such as regression, classification, and segmentation. It also tackles practical concepts, such as how to configure an end-to-end machine learning pipeline.

How You’ll Learn

The course is broken down into 7 sessions. Each session involves about 8 hours of work.

If you audit the course, you’ll have access to the course material for free, but not the assignments. If you subscribe, you’ll then have access to the assignments and a certificate at the end.

This course uses peer-feedback assignments, meaning your copy will be graded by other students and, in turn, you’ll grade other students to get your grade back.

Fun Facts

• This course has an 4.8 / 5.0 rating on Class Central with 80+ ratings, making it one of the highest-rated machine courses on the platform.
• It’s a course that mixes machine learning and art… need I say more!?

If you're interested in this course, you can find more information about the course and how to enroll here.

CS50: The Original Course

CS50 is taught by Harvard Professor David J. Malan. Back in 2015, when Class Central contributor Charlie Soliman reviewed the course, he said this about Professor Malan:

At the risk of stating a cliché, his approach to teaching is unlike anything I have seen and can easily be labeled as revolutionary. One can detect the joy and effort he puts into each of his lectures.

Having taken the course myself, I can say that this quote is as relevant today as it was back then. In fact, since the course is updated every year, it’s gotten even better.

CS50 in Harvard’s Sanders Theater

Offered at Harvard since 2007 and on edX since 2012, CS50 has grown to become Harvard’s largest on-campus course, with 800+ students per term. It's also one of the world’s highest rated and most popular MOOCs ever, with 2M+ learners in total.

And “CS50” has evolved from a code designating a single course to a brand encompassing multiple courses exploring different subjects, targeting different audiences, and stacking into different learning paths.

To make sense of CS50’s offerings, I partly audited each course, I explored their online communities, and I contacted their course staff. I found that Professor Malan often replies to emails himself — a nice touch considering the course has over 100 staff.

CS50: The Expanded Offering

CS50’s full offering currently comprises ten courses that can be broken down into three levels as shown in the table below. As you can see, seven courses offer a free certificate.

Click on a course or level to jump to the corresponding section.

Basic Course

Currently, CS50 only offers one basic course. It can be seen as an optional on-ramp to CS50’s core courses. If you don’t feel ready for a formal introduction to computer science, this is a good place to start.

CS50 Understanding Technology — CS50T

CS50T is a gentle introduction to the world of computing. It starts at the hardware level and works its way up from there. It explores how the internet works, how websites are created, how security is ensured, and ends with coding basics.

The course is taught by David J. Malan. It involves about 4 hours of study per week over 6 weeks. This course offers the following certificate options:

• Free certificate via Harvard OpenCourseWare
• Paid verified certificate via edX
• Paid transcript and academic credit via Harvard Extension School

Core Courses

Currently, CS50 offers four core courses. These include the original CS50 as well as variants geared toward different types of learners. This is where most learners want to start.

One course should be plenty. But learners that take a variant may want to also take the original CS50 — especially if they plan to continue with a follow-up course.

CS50 Introduction to Computer Science

CS50 is Harvard’s computer science introduction. It starts with binary and goes up the ladder of abstraction from machine code to low-level languages to high-level languages. It explores algorithms, data structures, and memory management. And it ends with one of three specialization tracks: web, game, or mobile development.

The course is taught by David J. Malan. It involves about 12 hours of study per week over 12 weeks. And it offers the following certificate options:

• Free certificate via Harvard OpenCourseWare
• Paid verified certificate via edX
• Paid transcript and academic credit via Harvard Extension School

CS50 AP Computer Science Principles — CS50AP

CS50AP is a CS50 variant that allows students in participating schools to earn AP credit. In the US, AP credit can help university applicants bolster their chances of admission and validate credit.

The course covers the same content as CS50 but includes more material, two extra assignments, and a proctored exam.

The course is taught by David J. Malan. It involves about 5 hours of study per week over 32 weeks. And it only offers a paid verified certificate via edX.

CS50 Computer Science for Lawyers — CS50L

CS50L is a CS50 variant geared toward lawyers and law students. Part of the curriculum explores similar content but emphasizes big-picture understanding of low-level details. The other part explores entirely new content that considers how law and computer science interact — for instance, in regards to cybersecurity, data privacy, and internet regulation.

The course is taught by David J. Malan and Doug Lloyd. It involves about 4 hours of study per week over 10 weeks and offers the following certificate options:

• Free certificate via Harvard OpenCourseWare
• Paid verified certificate via edX
• Paid certificate via Harvard Law School Executive Education

CS50 Computer Science for Business Professionals — CS50B

CS50B is a CS50 variant geared toward business professionals. Most of the course explores similar content but emphasizes big-picture understanding of low-level details. A small part explores entirely new content relevant to businesses, for instance, cloud computing.

The course is taught by David J. Malan. It involves about 4 hours of study per week over 6 weeks and offers the following certificate options:

• Free certificate via Harvard OpenCourseWare
• Paid verified certificate via edX
• Paid certificate and academic credit via Harvard Extension School

Follow-up Courses

Currently, CS50 offers five follow-up courses. These pick up where CS50 leaves off and explore a specific subject. They’re an excellent way to build on your computer science fundamentals by delving into a specialization.

CS50 Introduction to Artificial Intelligence with Python — CS50AI

CS50AI leverages the Python programming language to explore modern artificial intelligence. It covers foundational AI concepts, such as search algorithms and knowledge models, and builds on them to discuss more advanced concepts, such as optimization and machine learning.

The course is taught by Brian Yu. It involves about 20 hours of study per week over 7 weeks and offers the following certificate options:

• Free certificate via Harvard OpenCourseWare
• Paid verified certificate via edX
• Paid transcript and academic credit via Harvard Extension School

The courses is also part of edX’s Professional Certificate in Computer Science for Artificial Intelligence.

CS50 Web Programming with Python and JavaScript — CS50W

CS50W explores the languages, tools, and processes underpinning modern web app development. It covers languages such as Python and JavaScript, frameworks such as Flask and Django, and services such as GitHub and Heroku.

The course is taught by Brian Yu. It involves about 8 hours of study per week over 12 weeks. This course offers the following certificate options:

• Free certificate via Harvard OpenCourseWare
• Paid verified certificate via edX
• Paid transcript and academic credit via Harvard Extension School