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Quick Navigation:
- Fundamentals
- Research Methods
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- Inclusion
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- Podcasts and News Feeds
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- Research Papers
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- Benchmarks
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- Projects
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CS50: Computer Science Courses and Programs from Harvard If you have never programmed before, are new to computer science, or just want something to go back to the absolute basics, this is the online course for it. Hugely famous amongst the online CS community and majority of the videos are freely available both on the platform and also YouTube. The interactive practice sessions are also (usually) free, but certification is not.
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HyperSkill An interactive programming platform with practice streaks, online code editor and mobile app. Good for on the go practice and has Python and AI specific learning paths. Has a premium version but at the time of writing the free tier is more than adequate to get basic programming skill practice.
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NumPy NumPy is a mathematical package in Python which is at the core of many machine learning and AI models as it makes handling arrays, matrices and other forms of data far easier. It's usually an immediate first step after basics of Python. Don't delay on working through some of the beginner sessions that suit you best.
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pandas pandas is a fundamental Python package for handling data structures. It's one of the most common used in ML for handling and analysing data. Learning the basic syntax of Python is always your first step, but after a while, you need to start doing some data handling before you start getting stuck with AI models. This is a short but good enough intro to get you familiar with the package.
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Scikit-learn Crash Course - Machine Learning Library for Python Scikit-Learn is a powerful machine learning library for Python that supports both supervised and unsupervised learning algorithms. It's a really good starting point to start observing and learning about feeding data into basic ML algorithms and seeing some basic results - it will put a lot of things into context when reading papers, books, and doing course on ML/AI.
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PyTorch Tutorials PyTorch is the main framework used for AI/ML in research. The official tutorial series is good and worth spending the time going through once you've done some basic Python practice. Check out the YouTube playlist aswell! This resource is both in the ML and Programming resource lists in this repo.
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GitHub Tutorials GitHub is where you will be finding, reading, contributing, downloading and using for your AI/ML research and group projects. It's worth learning the basics early on!
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Git and GitHub for Beginners - Crash Course Alternative video version for learning GitHub basics. Don't let the length of the video scare you - take it one step at a time, it's worth it!
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🤗 Datasets Tutorials & Documentation Learn the basics and become familiar with loading, accessing, and processing a dataset. Start here if you are using 🤗 Datasets for the first time!
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Anaconda Beginners Guide for Linux, Mac and Windows - Python Working Environments Tutorial Anaconda (aka Conda) is a virtual environment management toolkit. When programming in Python, especially with complex AI/ML systems, you often need to draw on lots of libraries (packages) which makes your program run. But this also means your program is dependent on these libraries being present and this varies for each type of model. Virtual environments is a way of managing these virtual environments and is essential to understand in order to make things easier for you!
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Conda Cheat Sheet Quick and easy cheat sheet of common conda environment commands. Worth a bookmark.
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Getting started with conda "Conda is a powerful command line tool for package and environment management that runs on Windows, macOS, and Linux. This guide to getting started with conda goes over the basics of starting up and using conda to create environments and install packages."
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Artificial Intelligence by CrashCourse A very simple and basic-level introduction to AI (explain it to my like I'm six). If you've not really dived into AI before, definitely watch this playlist!!!
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Generative AI exists because of the transformer An accessible and interactive article by The Finanical Times on one of the biggest breakthroughs in AI, the Transformer.
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DeepLearning.AI These are short courses and cover a wide range of AI topics. Explore the beginner courses first before the other steps as it will give you a taste of what the field is about and like. Especially the AI for Beginners course: https://www.deeplearning.ai/short-courses/ai-python-for-beginners/
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5-Day Gen AI Intensive Course with Google Learn Guide This course needs a little bit of Python experience to get the most out of it, but still really handy to get a "feel" for generative AI and prompting models.
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Machine Learning Guide A great podcast series which builds up from zero to machine learning engineer topics. It provides lots of advice about starting out, what certain things means and routes into the field and lots of resources are provided.
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Practical Deep Learning for Coders "A free course designed for people with some coding experience, who want to learn how to apply deep learning and machine learning to practical problems." Recommended that you do some Python practice before this but you don't have to be a "programmer" to work your way through it.
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Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD The accompanying book that goes with the fast.ai course above. Recommended that you do some Python practice before this but you don't have to be a "programmer" to work your way through it. The eBook version is free online or you can order a physical copy.
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Machine Learning Specialisation by Andrew Ng The "famous" course series by Andrew Ng. The video content is free to access but the programming practice labs in Python you have to pay for.
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Machine Learning by StatQuest A widely popular online video series which is light hearted but still goes through core machine learning and data science topics. Very visual and easy to watch.
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The 100 Page Machine Learning Book An excellent, concise, overview of machine learning. The core principles, maths, some code, methodologies and applications. Useful to have in the library to quickly reference or refresh.
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The 100 Page Language Models Book Following on from the previous book (The 100 Page ML Book), the same author uses the same approach and format for tackling language models.
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PyTorch Tutorials PyTorch is the main framework used for AI/ML in research. The official tutorial series is good and worth spending the time going through once you've done some basic Python practice. Check out the YouTube playlist aswell! This resource is both in the ML and Programming resource lists in this repo.
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🤗 HuggingFace: LLM Course "This course will teach you about large language models (LLMs) and natural language processing (NLP) using libraries from the Hugging Face ecosystem — 🤗 Transformers, 🤗 Datasets, 🤗 Tokenizers, and 🤗 Accelerate — as well as the Hugging Face Hub." To get the most out of the HuggingFace courses, its usually best to have some of the basics of ML and Python programming experience.
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🤗 HuggingFace: Model Context Protocol (MCP) Course "This free course will take you on a journey, from beginner to informed, in understanding, using, and building applications with MCP." To get the most out of the HuggingFace courses, its usually best to have some of the basics of ML and Python programming experience.
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🤗 HuggingFace: AI Agents Course "📖 Study AI Agents in theory, design, and practice. 🧑💻 Learn to use established AI Agent libraries such as smolagents, LlamaIndex, and LangGraph. 💾 Share your agents on the Hugging Face Hub and explore agents created by the community. 🏆 Participate in challenges where you will evaluate your agents against other students’. 🎓 Earn a certificate of completion by completing assignments." To get the most out of the HuggingFace courses, its usually best to have some of the basics of ML and Python programming experience.
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🤗 HuggingFace: Deep Reinforcement Learning Course "📖 Study Deep Reinforcement Learning in theory and practice. 🧑💻 Learn to use famous Deep RL libraries such as Stable Baselines3, RL Baselines3 Zoo, Sample Factory and CleanRL. 🤖 Train agents in unique environments such as SnowballFight, Huggy the Doggo 🐶, VizDoom (Doom) and classical ones such as Space Invaders, PyBullet and more. 💾 Share your trained agents with one line of code to the Hub and also download powerful agents from the community. 🏆 Participate in challenges where you will evaluate your agents against other teams. You’ll also get to play against the agents you’ll train. 🎓 Earn a certificate of completion by completing 80% of the assignments." To get the most out of the HuggingFace courses, its usually best to have some of the basics of ML and Python programming experience.
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🤗 HuggingFace: Community Computer Vision Course "Computer vision is revolutionizing our world in many ways, from unlocking phones with facial recognition to analyzing medical images for disease detection, monitoring wildlife, and creating new images." To get the most out of the HuggingFace courses, its usually best to have some of the basics of ML and Python programming experience.
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🤗 HuggingFace: Audio Course "Time and again transformers have proven themselves as one of the most powerful and versatile deep learning architectures, capable of achieving state-of-the-art results in a wide range of tasks, including natural language processing, computer vision, and more recently, audio processing." To get the most out of the HuggingFace courses, its usually best to have some of the basics of ML and Python programming experience.
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🤗 HuggingFace: Open-Source AI Cookbook "The Open-Source AI Cookbook is a collection of notebooks illustrating practical aspects of building AI applications and solving various machine learning tasks using open-source tools and models." To get the most out of the HuggingFace courses, its usually best to have some of the basics of ML and Python programming experience.
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🤗 HuggingFace: Machine Learning for Games Course "the course that will teach you the most fascinating topic in game development: how to use powerful AI tools and models to create unique game experiences." To get the most out of the HuggingFace courses, its usually best to have some of the basics of ML and Python programming experience.
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🤗 HuggingFace: Diffusion Models Course "👩🎓 Study the theory behind diffusion models 🧨 Learn how to generate images and audio with the popular 🤗 Diffusers library 🏋️♂️ Train your own diffusion models from scratch 📻 Fine-tune existing diffusion models on new datasets 🗺 Explore conditional generation and guidance 🧑🔬 Create your own custom diffusion model pipelines" To get the most out of the HuggingFace courses, its usually best to have some of the basics of ML and Python programming experience.
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🤗 HuggingFace: Machine Learning for 3D Course "In this course, you’ll learn: What’s going on - the current big picture of machine learning for 3D Why it matters - the importance of recent developments How to do it yourself - build your own generative 3D demo" To get the most out of the HuggingFace courses, its usually best to have some of the basics of ML and Python programming experience.
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Khan Academy Regardless of your mathematical experience, Khan Academy is a renowned platform for its bitesize and easy to dip into way of teaching maths. Everything from basic addition up to linear algebra, vectors, matrices and advanced calculus. If you think you want to brush up some skills or learn for the first time, it's a definite bookmark. The mobile app is handy for practice on the go too!
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Why Machines Learn by Anil Ananthaswamy: The Elegant Maths Behind Modern AI An amazing book which explains the how and why behind machine learning mathematics. It builds up knowledge from the ground up, not requiring a huge amount of priro mathematical experience. The information is provided both with compelling visuals and also some storytelling behind the resultant maths in ML.
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Calculus Made Easy by Silvanus Thompson Alternative approach and methodology of thinking about calculus. Very good worked examples and visualisations of mathematics. Not all examples relevant to AI/ML but still helpful. Digital version free.
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Math, Better Explained Another maths explainer with a very approachable style. Not much free content though and will require some foundational algebra experience to get the most out of it.
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University of Cambridge: Inclusive Design Toolkit "The design of mainstream products and/or services that are accessible to, and usable by, as many people as reasonably possible ... without the need for special adaptation or specialised design." A good resource for an intro to inclusive design and some case studies in tech.
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Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy by Cathy O'Neil "Tracing the arc of a person's life, Cathy O'Neil exposes the black box models that shape our future as individuals and as a society. These "weapons of math destruction" score teachers and students, sort CVs, grant or deny loans, evaluate workers, target voters and monitor our health."
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Mismatch: How Inclusion Shapes Design by Kat Holmes "Sometimes designed objects reject their users: a computer mouse that doesn't work for left-handed people, for example, or a touchscreen payment system that only works for people who read English phrases, have 20/20 vision, and use a credit card."
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Hello World: How to Be Human in the Age of the Machine by Hannah Fry "It’s a book about how we’ve slowly handed over control to computers – how there are algorithms and artificial intelligence hiding behind almost every aspect of our modern lives – and what that means for our society."
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Unreal Engine by Epic Games Epic Games' online library of short to long form tutorials are extremely thorough and high quality, often right up-to-date with the latest engine versions and releases. The "Getting Started" page offers Games, Film & TV, Architecture and Visualization as initial learning pathways.
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Unity Another popular commerical game engine. Has "learning paths" for those interested in diving straight into 3D game and asset creation.
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GODOT: Your free, open‑source game engine A very popular alternative to commercial game engines. Large community of contributors, however, lacks some of the polished tutorials and learning platforms offered by others. Does have a large volume of unofficial YouTube video playlists available.
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Blender "Blender is a free and open-source 3D computer graphics software tool set that runs on Windows, macOS, BSD, Haiku, IRIX and Linux. It is used for creating animated films, visual effects, art, 3D-printed models, motion graphics, interactive 3D applications, and virtual reality. It is also used in creating video games."
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TouchDesigner "TouchDesigner is a node-based visual programming language for real-time interactive multimedia content. Developed by the Toronto-based company "Derivative," it's often used by artists, programmers, creative coders, software designers, and performers to create performances, installations, and fixed media works." It has a paid/premium tier but also a free version available for personal and educational use, with some limitations.
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Machine Learning Street Talk (MLST) A media production company making video, audio and written content about machine learning, cognitive science and cognitive philosophy. The podcast is the highest-rated technical AI podcast on Spotify and is renowned on YouTube as well: https://www.youtube.com/@MachineLearningStreetTalk
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Machine Learning Guide/Applied "MLG is a machine learning podcast teaching the fundamentals of machine learning and artificial intelligence. It covers intuition, models, neural networks, math, languages, frameworks, and more. Podcasts are a great supplement during exercise, commute, chores, etc. The resources section provides a syllabus for machine learning videos, courses, books, and audio."
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The AI Daily Brief The most important news and discussions in AI" A great snippet and short form up-to-date resource on big AI trends.
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The Batch: by Andrew Ng A very good weekly write up from Andrew Ng on some of the latest trends and interesting AI papers. Written in a very accessible way.
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HuggingFace Daily Papers "Get trending papers in your email inbox once a day!"
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Towards Data Science: The Variable Independent writers in data science and machine learning author lots of intro to advance artricles.
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