Best Machine Learning & AI courses
The 172 Machine Learning & AI courses programmers recommend to each other on Hacker News, ranked by how many people recommended each one, how strongly, in how many separate threads and over how many years (846 recommendations, 2006 to October 2026). 119 of them are free. How ranking works.
Machine Learning (Andrew Ng)
Coursera · beginner · free
Commenters call it a great introduction that avoids heavy math while still conveying real understanding of the topic, and many say it was their starting point.
Practical Deep Learning for Coders
fast.ai · beginner · free
Commenters repeatedly call it the best practical entry point into deep learning for programmers, praising its focus on building working models quickly without requiring a heavy math background.
CS231n: Convolutional Neural Networks for Visual Recognition
Stanford University · intermediate · free
Commenters repeatedly call it an excellent, hands-on introduction to deep learning and neural networks, praising the course notes, assignments, and Karpathy's teaching.
Deep Learning Specialization (Andrew Ng)
Coursera · intermediate · paid
Commenters call it excellent and phenomenal, praise its theory-first approach that builds understanding of what happens under the hood, and say the assignments help clarify concepts.
Neural Networks for Machine Learning (Geoffrey Hinton)
Coursera · advanced · free
Commenters call it an excellent, challenging introduction to neural networks taught by a pioneer, and several say it changed the course of their careers.
CS229 Machine Learning
Stanford University · advanced · free
Commenters call it one of the best ML courses out there, praised for deriving algorithms from the math and for its clear, rigorous treatment compared with the lighter Coursera version.
Learning From Data
Caltech · intermediate · free
Commenters call it an outstanding, rigorous introduction to the theory behind machine learning, more mathematical than Andrew Ng's Coursera class and taught with exceptional clarity.
CS188 Introduction to Artificial Intelligence
UC Berkeley · intermediate · free
Commenters call it one of the best online courses they've taken, praising instructor Dan Klein and the Pacman programming assignments.
Probabilistic Graphical Models
Coursera · advanced · free
Commenters call it a very good, rigorous deep dive into graphical models, with several saying it is worth the effort despite rough edges in the first run.
Neural Networks: Zero to Hero
YouTube · beginner · free
Commenters call it a clear, hands-on way to understand backpropagation and transformers, with Karpathy's explanations of attention singled out as especially accessible.
Intro to Artificial Intelligence
Udacity · beginner · free
Commenters call it an excellent, clear introduction to AI, praising Peter Norvig's teaching, though some note errors and a lack of polish.
Artificial Intelligence for Robotics
Udacity · intermediate · free
Commenters call it an accessible, hands-on introduction to probabilistic methods like Kalman filters, with Thrun praised for explaining intuition clearly.
Self-Driving Car Engineer Nanodegree
Udacity · intermediate · paid
Commenters call it the best hands-on course they have taken and say it lets you build real self-driving software, including a TensorFlow project.
Reinforcement Learning (David Silver)
YouTube · intermediate · free
Commenters call it excellent and great, praise Silver as an amazing teacher whose visual explanations make the Bellman equation click, and often pair it with Sutton and Barto.
CS224N: Natural Language Processing with Deep Learning
Stanford University · advanced · free
Commenters point to it as the go-to deep-learning-focused NLP course, with one calling it a good high-level grounding and another praising its historical perspective and lectures on word vectors.
6.034 Artificial Intelligence
MIT OpenCourseWare · intermediate · free
Commenters call Patrick Winston's lectures brilliant and say the course is worth doing in full, with one praising OCW's offering as still a great resource.
CS224d: Deep Learning for Natural Language Processing
Stanford University · advanced · free
Commenters call it a great course on NLP with deep learning, recommended as a companion to cs231n for going deeper into neural networks for language.
Bitcoin and Cryptocurrency Technologies
Coursera · beginner · free
Commenters call it a good primer that explains the concepts clearly, with one saying it was superb and has stuck with them for years.
Statistical Learning (Hastie and Tibshirani)
Stanford · beginner · free
Commenters call it a terrific, practical base in classical statistical learning that is worth more than many ML or deep learning courses for data science work and interviews.
Data Science Specialization
Coursera · beginner · paid
Commenters recommend it as a good way to learn R alongside data science and machine learning fundamentals, though one notes it is good rather than great.
The Analytics Edge
edX · beginner · free
Commenters call it a great MOOC and the best analytics course they've taken, praising its hands-on exercises and above-average MITx quality.
Learning from Data
edX · intermediate · free
Commenters call it far and away the best MOOC they have taken, praising its challenging, mathematically grounded approach over library-API tutorials.
Machine Learning Specialization (University of Washington, Carlos Guestrin and Emily Fox)
Coursera · beginner · freemium
Commenters call it well organized with good lectures, and say it explains the math behind the algorithms in a rigorous but approachable way.
Deep Learning (ud730)
Udacity · intermediate · free
Commenters recommend it as a hands-on deep learning course, with one calling it the Google deep learning class and another highly recommending it for beginners.
Machine Learning
Udacity · beginner · free
Commenters call the Udacity ML and AI courses too good for the price of nothing and a practical, hands-on way to start with machine learning.
Cryptography II
Coursera · intermediate · free
Commenters call it a strong follow-on to the first course and say Boneh's Coursera crypto material is among the best MOOCs they have taken.
Basic Modeling for Discrete Optimization
Coursera · beginner · free
Commenters call it an excellent introduction to constraint programming and a good way to learn MiniZinc, and one recommends it along with its sequel.
Reinforcement Learning Specialization
Coursera · intermediate · freemium
Commenters call it a very good set of RL courses and say it closely follows the Sutton and Barto book with quizzes and assignments, unlike pure video courses.
Deep Learning Nanodegree
Udacity · intermediate · paid
Commenters call it much better than a university lecture course and say it has the best explanations and notebooks for building neural networks from scratch.
Introduction to Data Science
Coursera · beginner · free
Commenters call it a practical, reasonably paced starting point that builds real working knowledge and prepares learners for more advanced courses.
CS373 Programming a Robotic Car
Udacity · intermediate · free
Commenters describe it as a strong, well-paced introduction to autonomous robotics, with one saying Thrun claims its coursework matches or exceeds his Stanford classes at the same level.
ChatGPT Prompt Engineering for Developers
DeepLearning.AI · beginner · free
Commenters recommend it as a quick, practical entry point for building LLM-based apps, and note it is approachable even with only basic Python skills.
Introduction to Deep Learning (6.S191)
MIT · beginner · free
Commenters describe it as a very good, gentle introduction that builds from the ground up to transformers and attention.
Deep Learning
Carnegie Mellon University · intermediate · free
Commenters call it a very good open course on deep learning and say it is great, with one noting its lecturer Bhiksha Raj.
MiniZinc Constraint Programming
Coursera · beginner
Commenters call the Coursera courses an excellent, very effective introduction to MiniZinc and combinatorial constraint modeling.
Machine Learning (Georgia Tech OMSCS)
Udacity · advanced · free
Commenters call it one of the best courses in the online master's, praising its focus on foundational concepts and its usefulness at work.
Deep Learning (Nando de Freitas)
YouTube · intermediate · free
Commenters call it a great lecture series for learning deep learning, noting it is a bit math heavy but has a really nice derivation of backprop.
CS 294 Deep Reinforcement Learning
UC Berkeley · advanced · free
Commenters call it a great, comprehensive, up-to-date lecture course taught by Sergey Levine, one of the researchers they consider foundational in deep RL.
Natural Language Processing (Dan Jurafsky and Chris Manning)
YouTube · beginner · free
Commenters point to it as a strong free introduction to NLP from the authors of a well-regarded textbook, with one calling it a great online course and another strongly recommending it as a mini-course.
Intro to Self-Driving Cars
Udacity · intermediate · free
Commenters describe it as a great starting point for autonomous cars and a world-class computer-science education available for free from a leading AI expert.
CS330 Deep Multi-Task and Meta Learning
Stanford University · advanced · free
Commenters describe it as a one-stop resource for multi-task and meta-learning and list it among Stanford's excellent free AI course notes and lectures.
Control of Mobile Robots
Coursera · intermediate · free
Commenters call it a strong option for the basics of robot control, with one saying it covers odometry and PID control better than the Udacity robotics course.
CS221: Artificial Intelligence: Principles and Techniques
Stanford University · intermediate · free
Commenters call it practical and kept up to date, with strong lecturers, and say it is a required companion to CS 229 for Stanford's AI track.
Deep Reinforcement Learning (CS 285)
UC Berkeley · advanced · free
Commenters call it extremely insightful and highly recommend it, and point out that the slides, lectures, and assignments are all public.
AI for Trading
Udacity · intermediate · paid
Commenters call it the best course they have taken and say they doubt a comparable course exists elsewhere.
Learning From Data (Yaser Abu-Mostafa)
Caltech / edX · beginner · free
Commenters point to it as a free, accessible glimpse into machine learning taught by an excellent professor.
Introduction to Artificial Intelligence
Stanford · intermediate · free
Commenters call it a strong example of high-quality free online instruction from a top university, and one commenter says it pairs well with Norvig's textbook.
Deep Learning Course (Hugo Larochelle)
YouTube · beginner · free
Commenters call it a very good, great-for-beginners reference for deep learning, often placed alongside Hinton's Coursera course and Nando de Freitas's lectures.
Elements of AI
University of Helsinki · beginner · free
Commenters call it accessible and a good introduction to AI, and one recommends it to non-technical executives as a less technical alternative to Andrew Ng's Coursera courses.
3Blue1Brown Neural Networks
YouTube · beginner · free
Commenters call it an extremely brilliant, concise high-level overview of how neural networks work and recommend starting there.
CS238 Decision Making under Uncertainty
Stanford University · advanced · free
Commenters describe it as one of their favorite courses and point to Kochenderfer's material as the natural next step after general game playing, extending to state-of-the-art approaches.
Natural Language Processing (Stanford)
Coursera · intermediate · free
Commenters describe it as an excellent overview that serves as a good entry point into NLP papers and literature.
10-701 Introduction to Machine Learning
Carnegie Mellon University · intermediate · free
Commenters call it a fantastic course, especially for its exercises, and note that its lectures and materials are freely available online.
10-601 Machine Learning
Carnegie Mellon University · intermediate · free
Commenters describe it as a rigorous, hard but worthwhile introduction to ML, and one suggests it as the first course to take.
Natural Language Processing (Michael Collins)
Coursera · intermediate · free
Commenters point to it as a solid, research-grounded NLP course from a well-known researcher in the field, alongside his lecture notes.
CS336: Language Modeling from Scratch
Stanford University · advanced · free
Commenters call it a deep, theory-and-systems-oriented route into LLMs, with one saying the lecture notes made computational bottlenecks much more intuitive.
Introduction to Analytics Modeling
edX · intermediate
Commenters call it a standout online course with an excellent lecturer and a useful overview of how to select and compare analytics methods.
Hugging Face NLP Course
Hugging Face · intermediate · free
Commenters call it a very good, hands-on starting point for NLP and transformer models, and suggest it as the place to start among Hugging Face's courses.
Practical Machine Learning
fast.ai · beginner · free
Commenters recommend it for learning the how-to of ML and getting into the nitty gritty of training neural networks, with little time spent on theory.
Python for Data Science and Machine Learning
Udemy · beginner · paid
Commenters describe it as a good hands-on overview of current machine learning topics and praise Portilla's courses for covering the applied aspects well.
Introduction to Computer Vision
Udacity · beginner
Commenters describe it as a great class for getting into computer vision and call it a very good course on the subject.
Audio Signal Processing for Music Applications (Xavier Serra)
Coursera · intermediate
Commenters describe it as a practical, hands-on class with lots of programming examples and useful open-source software tools, and a fun follow-up after a signal processing course.
Practical Data Science
Carnegie Mellon University · beginner · free
Commenters describe it as a current, practical intro to data science with a lot of machine learning and big data, and a good crash course for self-taught developers.
Programming a Self-Driving Car
Udacity · advanced · free
Commenters bring it up as a source of machine learning material on Udacity that is hard to find elsewhere, such as self-driving car topics alongside conv nets for object recognition.
10-702 Statistical Machine Learning
Carnegie Mellon University · advanced · free
Commenters present it as the theory-focused next step after the intro course, suited to anyone wanting a solid grounding in the statistical side of ML.
Machine Learning
University of Oxford · intermediate · free
Commenters recommend it as a free, high-quality alternative to short introductory courses, with one calling it perhaps the most recent ML MOOC available.
LangChain short course
DeepLearning.AI · beginner · free
Commenters recommend it as a quick, practical starting point for building LLM-based apps, with one noting it is accessible with only basic Python skills.
Neural Networks and Deep Learning
Coursera · beginner · freemium
Commenters call it a good beginner-friendly path into deep learning, with the math taught alongside the material.
Recommender Systems Specialization
Coursera · intermediate
Commenters point to it as a good dedicated course on recommendation systems and note that it covers model evaluation well, which they say many algorithm-focused posts skip.
Machine Learning (Tom Mitchell)
Carnegie Mellon University · intermediate · free
Commenters say it gives a much better understanding of the algorithms than Andrew Ng's course, which they describe as good for implementation but light on insight.
PyTorch for Deep Learning Professional Certificate
DeepLearning.AI · intermediate
One commenter calls it probably one of the best MOOCs they have seen and highly recommends it as a deeper follow-up to a visual PyTorch introduction.
CS236: Deep Generative Models
Stanford University · advanced · free
Commenters call Stefano Ermon's lectures amazing and point to the full YouTube series as a video option for learning generative AI.
Machine Learning (teacher professional development)
The Coding School
One commenter calls it an excellent course and notes it earned 4 CEUs from Stanford.
Karpathy lecture on attention and transformers
YouTube · intermediate · free
The commenter calls it the best video for learning attention, praising how it explains queries, keys, and values as message passing between vectors.
Full Stack Deep Learning
Full Stack Deep Learning · intermediate
One commenter says they took the course several years ago at Berkeley and highly recommends it.
STAT 453: Introduction to Deep Learning and Generative Models
University of Wisconsin-Madison · intermediate · free
One commenter says it is really good and that they have gone through it three times.
Deep Learning (Yann LeCun and Alfredo Canziani)
New York University · advanced · free
Commenters call it the single best freely available lecture series on non-beginner deep learning, though one warns it is dense and easy to get lost in.
CS 474 Introduction to Computer Vision
University of Nevada, Reno · intermediate · free
A commenter who took the related CS 674 course at UNR says they would highly recommend it and notes it covered most of the material in the H.264 article plus more.
Blockchains and Cryptocurrencies
MIT OpenCourseWare · intermediate · free
One commenter calls it a highly recommended video course on blockchains, pointing to the MIT lecture playlist.
Cengiz Pehlevan Harvard Course
Harvard University · intermediate · free
A commenter who just finished the class calls it fantastic and shared its lectures and exercises with the forum.
PyTorch video course
YouTube · beginner · free
Commenters call it the best video course they found for learning PyTorch, praising how well it explains things and how easy and engaging the videos are.
Azure Machine Learning
edX · beginner · freemium
One commenter calls Microsoft's edX MOOCs, including the Azure Machine Learning course, among the best MOOCs they have taken, citing their narrow focus and realistic time estimates.
Knowledge-Based Artificial Intelligence
Udacity · intermediate · free
One commenter calls it a fantastic course, praising its deep, theory-focused commentary on what knowledge, intelligence, and learning mean for a program.
Information Theory, Inference, and Learning Algorithms (David MacKay)
University of Cambridge · intermediate · free
Commenters say David MacKay's lectures are incredible and explain the material more understandably than his book.
CS143 Introduction to Computer Vision
Brown University · beginner · free
One commenter, a computer vision researcher, says they highly recommend the online materials for learning computer vision in detail.
CS4670 Computer Vision
Cornell University · beginner · free
One commenter, a computer vision researcher, highly recommends it as an online resource for learning the details of computer vision, alongside a Brown course and Szeliski's book.
Artificial Intelligence (Andrew Moore)
Carnegie Mellon University
Commenters call the lectures the best they have seen, based on one commenter who took Andrew Moore's AI course at CMU.
Data Science (datasciencecourse.org)
datasciencecourse.org
Commenters point to it as a hands-on, practical data science course that suits someone who already knows Python.
Post-training of LLMs
DeepLearning.AI · intermediate
A commenter recommends it as a way to understand what happens after pre-training, namely the RLHF and alignment work that makes LLMs useful.
CS885 Reinforcement Learning
University of Waterloo · advanced · free
Commenters point out that it covers Bayesian reinforcement learning, a topic missing from most other RL courses.
Reinforcement Learning and Optimal Control
MIT OpenCourseWare · advanced
The commenter calls Bertsekas's lectures on reinforcement learning and related topics the best available.
Natural Language Processing Specialization
DeepLearning.AI · intermediate
Commenters point to DeepLearning.AI's resources as very good, based on a prior course from the same provider, and suggest this NLP specialization as a follow-on.
A Hacker's Guide to Language Models
YouTube · beginner · free
One commenter points to it as a fantastic, practical video on how to use LLMs, recommended alongside Karpathy's neural networks course.
Large Language Models: Application through Production
edX
One commenter recommends it as a follow-on for readers who are still interested in LLMs after working through foundational programming courses.
16-385 Computer Vision
Carnegie Mellon University · beginner · free
Commenters point to it as the starting place for learning computer vision, especially for building basic vision terminology before moving to deep learning.
Deep Learning Systems
Carnegie Mellon University · advanced · free
A commenter calls CMU's open deep learning courses very good and says this one dives much deeper into the internals of the libraries.
Building Systems with the ChatGPT API
DeepLearning.AI · beginner
Commenters recommend it as part of a suggested sequence for quickly building LLM-based apps, noting that basic Python skills are enough to follow along.
Data Analytics
edX
A commenter lists it among multidisciplinary self-study resources (scripting, visualization, models, simulation) that they credit with giving them a practical advantage at work.
CS25: Transformers United
Stanford University · intermediate · free
One commenter calls the Stanford courses, including the Transformer one, a safe bet among the many university lectures available online.
Stable Diffusion from the Ground Up
fast.ai · intermediate · free
One commenter calls the lectures a really good introduction to how Stable Diffusion works, recommending them after trying the AUTOMATIC1111 web UI.
GANs Specialization
DeepLearning.AI · intermediate
One commenter points to it as the place to go after the basics for learning GANs, noting it covers topics through StyleGAN.
CS224W: Machine Learning with Graphs
Stanford University · advanced · free
Commenters point to it as one of two good graph neural network courses, valued for its focus on ML applied to structured data.
Intro to GNNs (Zak Jost)
YouTube · beginner · free
Commenters recommend it as a second option alongside Stanford's CS224W for learning GNNs.
CS224U: Natural Language Understanding
Stanford University · advanced · free
Commenters list it among useful Stanford AI course notes and point to its coverage of language understanding, including a section on SHRDLU.
CS234: Reinforcement Learning
Stanford University · advanced · free
One commenter lists it as step two in a recommended sequence of Stanford courses and calls the lectures free and high-quality but demanding.
Probabilistic Machine Learning (University of Tübingen)
YouTube · advanced · free
One commenter says it probably has one of the most thorough walkthroughs of a latent factor model they've found online, and notes the lectures are free on YouTube.
Machine Learning Summer School lectures
MLSS · intermediate · free
A commenter says the lectures are often quite good because exceptionally talented researchers and practitioners give long talks on their own domains.
Machine Learning (Philipp Hennig)
YouTube · free
One commenter recommends it for machine learning, pointing to the lecture playlist from the University of Tübingen.
CS50's Introduction to Artificial Intelligence with Python
Harvard University · beginner · free
A commenter recommends the 2020 edition and shares their own notes from the course as a companion resource.
General Game Playing
Stanford · advanced
Commenters point to it as close to what the asker wants, a hands-on course on building game-playing AI such as a chess engine.
MathematicalMonk Machine Learning
YouTube · intermediate · free
One commenter shares it as a well-regarded video source for machine learning, describing the creator as someone who used to make really great ML videos.
Convolutional Neural Networks (Deep Learning Specialization)
Coursera · intermediate · freemium
Commenters say the course has been great and recommend working through its notebooks and the interviews with deep learning pioneers.
mlcourse.ai
mlcourse.ai · beginner · free
Commenters pitch it as a more modern, Kaggle-oriented complement to Andrew Ng's course, with interesting projects.
AI for Everyone
Coursera · beginner · free
Commenters point to it as a good starting course for people who need to understand AI in a business context without coding.
Reinforcement Learning (Georgia Tech OMSCS)
Udacity · advanced · free
A commenter highly recommends the course and notes that most of the Georgia Tech online CS master's classes are free on Udacity.
Computer Vision (Georgia Tech OMSCS)
Udacity · advanced · free
Commenters point to it as one of the Georgia Tech OMSCS courses available free on Udacity and say they highly recommend it alongside the ML and RL courses.
spaCy Course
spaCy · beginner · free
One commenter calls it an "extremely slick interactive course" and describes spaCy as a highly recommended Swiss Army knife library for NLP, recommended by its package author Ines Montani.
How to Build Your Own Self-Driving Vehicle
Udacity · intermediate · free
One commenter, who took it after the 2011 ML Class, says it taught them concepts they had struggled with before, including SLAM, A*-style path finding, and Kalman filtering, and calls the earlier series an excellent course.
Data 8: The Foundations of Data Science
UC Berkeley · beginner · free
One commenter points to it as the Berkeley intro to data science for people without programming or college math background, with an online book and notebooks.
How to Win a Data Science Competition: Learn from Top Kagglers
Coursera · intermediate
Commenters call it a great course for learning machine learning as practiced on Kaggle, and one says finishing it puts you ahead of many working data scientists.
Spinning Up in Deep RL
OpenAI · intermediate · free
One commenter suggests it as the next step after fast.ai, calling it OpenAI's crash course in reinforcement learning.
Flying Car and Autonomous Flight Engineer Nanodegree
Udacity · intermediate · paid
One commenter lists it alongside Udacity's robotics nanodegrees and says they highly recommend Udacity's recent paid robotics courses.
Deep Learning (Andrew Ng)
Coursera · beginner · paid
A commenter recommends it for picking up the principles of deep learning and for exposure to Keras and TensorFlow.
Convolutional Neural Networks
Udacity · free
One commenter lists conv nets for object recognition among the things Udacity offers that you would not get elsewhere, and says many of its free courses are superior.
Machine Learning Crash Course
Google Developers · beginner · free
One commenter calls it a good, more interactive alternative to books for getting started with machine learning, noting it leans toward TensorFlow.
Probabilistic Graphical Models Specialization
Coursera · advanced
One commenter recommends it as a MOOC for getting into graphical models and probabilistic programming, which they describe as a growing AI trend.
10-715 Advanced Introduction to Machine Learning
Carnegie Mellon University · advanced · free
Commenters point to it as a graduate ML course that is accessible with undergraduate probability, statistics, and linear algebra, and note that its recorded lectures are available.
Practical Deep Learning for Coders Part 2
fast.ai · intermediate · free
Commenters point to it as a practical, hands-on route into deep learning that spends very little time on theory and lets learners follow videos at their own pace.
Applied Data Science with Python
Coursera · beginner · freemium
The one commenter currently taking it says they like the use of Jupyter notebooks for assignments, which they find realistic yet accessible.
Artificial General Intelligence
MIT
A commenter recommends the entire course and specifically praises the guest lecturers.
Introduction to Hyperledger
edX · beginner · free
Commenters point to it as a free way to understand private blockchain technology and the Hyperledger projects before reading about Bitcoin and Ethereum.
Neural Networks and Deep Learning (Deep Learning Specialization)
Coursera · beginner · freemium
One commenter calls Andrew Ng's newer neural network specialization a great first step alongside his intro to machine learning course.
Turi Create Machine Learning Course
Coursera · beginner
Commenter recommends it for getting started with Turi Create, calling it a really good overview of how to use the library.
Machine Learning Foundations
Coursera · beginner
Commenters describe it as a very high-level, novice-friendly quick introduction to popular ML techniques and when to use them.
Intro to Text Mining: Bag of Words
DataCamp · beginner · freemium
A commenter recommends it as a practical starting point for learning how to convert words into variables for modeling when classifying text.
The Math of Intelligence
YouTube · beginner · free
One commenter recommends it as a YouTube resource for the math needed to get into ML/AI, alongside 3Blue1Brown.
CS6601 Artificial Intelligence
Georgia Tech · advanced · paid
Commenters single it out as one of the two best courses in the OMSCS program, praising how quickly the concepts could be applied at work.
Intro to Machine Learning
Udacity · beginner · free
One commenter calls it a really good introduction to SVMs and other basic machine learning concepts.
DAT208x Introduction to Data Science
edX · beginner · free
Commenters say the in-browser practical exercises help build a real grasp of the material.
CS229A Applications of Machine Learning
Stanford University · beginner · free
Commenters praise Andrew Ng's teaching as making machine learning engaging and practical, and one contrasts it favorably with Geoffrey Hinton's neural network course.
Machine Learning Nanodegree
Udacity · intermediate · paid
A commenter who completed it says it is worth doing for the structure and motivation it provides, though similar materials are available for free.
Machine Learning (Nando de Freitas)
YouTube · intermediate · free
One commenter says they liked it a lot and suggests it as an alternative for someone struggling with deep learning material.
STAT 946: Topics in Statistical Theory (Deep Learning)
University of Waterloo · advanced
Commenters suggest it alongside cs231n and cs224d as a useful deep learning course in a paper-reading roadmap.
Neural Networks (Hugo Larochelle)
Université de Sherbrooke · beginner · free
The commenter recommends it as a gentler entry point than Hinton's more theoretical Coursera course.
Machine Learning Crash Course with scikit-learn and TensorFlow
YouTube · beginner · free
One commenter calls it an extremely nice hands-on way to start with machine learning, using scikit-learn and TensorFlow.
CS189 Machine Learning
UC Berkeley · intermediate · free
Commenters point to it as one of the good structured courses for getting started in machine learning, alongside Andrew Ng's course.
6.803 The Human Intelligence Enterprise
MIT OpenCourseWare · advanced · free
One commenter calls it an incredible seminar-based AI reading class taught by Professor Winston, and links the schedule and papers from a recent semester.
Natural Language Processing (Stanford)
Stanford University
One commenter says they took the Stanford NLP course online and found it great, and another says they are currently taking it.
Machine Learning with TensorFlow
Udacity · intermediate
Commenters suggest it is best for learners who already know machine learning principles and want to apply them in a more professional toolchain with TensorFlow.
Machine Learning (Washington University)
Coursera · beginner · free
Commenters call it a solid foundation in ML and praise the lecturer as the best they have seen.
Intro to Data Science
Udacity · beginner · free
Commenters recommend it as a hands-on entry point, with one saying the Udacity courses are better than a traditional AI class for getting hands dirty fast.
Neural Network Demystified
YouTube · beginner · free
One commenter recommends it as a basic-level companion to Coursera-style material that still gets into the math.
AWS Course
Cloud Academy
One commenter calls it a great course for learning Amazon Machine Learning, with a link to the Cloud Academy AWS course page.
Variational Methods for Computer Vision
Computer Vision Talks · advanced
Commenters point to it as a good pick for anyone interested in variational approaches to computer vision, noting Dan Cremer's lectures.
Data Analyst Nanodegree
Udacity · paid
One commenter says Udacity nanodegrees are more valuable than other MOOCs because they are built with employers in mind and give structure, feedback, and a strong portfolio.
Mining Massive Datasets
Coursera · intermediate · free
One commenter recommends it as a way to learn the same concepts (PageRank, Jaccard similarity) covered in a blog post on Flipboard's summarization approach, taught by Stanford faculty.
Getting and Cleaning Data
Coursera · intermediate · freemium
Commenters find it fun and worthwhile, with one noting its project allowed freedom in approach as long as the reasoning was documented.
Exploratory Data Analysis
Coursera · beginner · freemium
One commenter, a programmer with 20+ years of experience, is enjoying it and says it is worth it for them, noting the projects are fun and the course builds toward machine learning.
EECS 568 Mobile Robotics
University of Michigan · advanced · free
One commenter calls Prof. Olson's video lectures excellent for learning probabilistic perception algorithms applied to robotics.
CS271 Introduction to Artificial Intelligence
Udacity · beginner · free
Commenters call it an excellent, clear introduction to AI taught by Thrun and Norvig, and several point to it as the natural next step after Andrew Ng's machine learning course.
Introduction to Artificial Intelligence
Coursera · beginner
Commenter calls it a very good survey of Artificial Intelligence for anyone who has never been exposed to the material.
CS129 Computational Photography
Brown University · intermediate
One commenter points to its first assignment as a manageable, good introduction to image alignment.
9.520 Statistical Learning Theory and Applications
MIT OpenCourseWare · advanced · free
One commenter calls the whole course great and points to its slides for an intuitive explanation of why projection onto principal components aids generalization.
Bayesian Learning (Zoubin Ghahramani)
VideoLectures.net · intermediate · free
One commenter calls it a solid lecture on Bayesian learning, though noting the speaker is not the most engaging.
11-761 Language and Statistics I
Carnegie Mellon University · advanced
Commenters call it a great course for anyone who wants to work primarily with text, since it covers the prerequisite machine learning as well.
6.867 Machine Learning
MIT OpenCourseWare · advanced · free
A commenter lists it among MIT courses they consider more relevant than the alternatives, alongside linear algebra and probability.
6.863J Natural Language Processing
MIT OpenCourseWare · advanced · free
One commenter, working on an MSc project that required reading NLP research, recommends the MIT computational linguistics lectures as a starting point, suggesting watching at least the first half dozen.
CS171 Visualization
Harvard University · intermediate · free
A commenter currently enrolled calls it very enriching and points others to it as a strong resource for learning visualization.
Machine Perception of Music and Audio (EECS 352)
Northwestern University · intermediate · free
Commenters point to its lecture notes as a clear source on spiral pitch representations and chromagrams.
Statistical Aspects of Data Mining
Google · intermediate · free
One commenter recommends it as a free lecture series alongside a free statistics book for someone wanting to learn statistics and data mining.
Principles of Data Mining
Princeton University · beginner
Commenters suggest it for people whose math background is weak, since the ratio of English to equations is high.
Stats 202
stats202.com · beginner · free
Commenters point to it as a free, R-based course built on a Stanford class, with recorded lectures available.