Free AI & Tech Courses
Hand-picked AI and tech courses, free and paid, that are worth your time.
135 courses · page 1 of 3
Finetuning Large Language Models
Explains when and why to fine-tune an LLM, how to prepare training data, and how to train and evaluate a model on your own examples. Aimed at learners comfortable with Python and basic deep learning.
View courseHow Diffusion Models Work
Build a diffusion model step by step to understand how image generators turn noise into pictures, including sampling, training and speed-ups. Best for learners with some Python and PyTorch experience.
View courseFunctions, Tools and Agents with LangChain
Covers function calling, the LangChain Expression Language, tool use, tagging and extraction, and building a conversational agent. Suited to developers who already know LangChain basics.
View courseVector Databases: from Embeddings to Applications
Understand how vector databases store embeddings and run fast similarity search, then apply them to RAG, recommendations and hybrid search. Good for developers and data engineers working with AI apps.
View courseAI Agents in LangGraph
Build an AI agent from scratch, then rebuild it with LangGraph, adding agentic search, persistence, streaming and human-in-the-loop control. For developers who want to create dependable agent workflows.
View courseMCP: Build Rich-Context AI Apps with Anthropic
Learn the Model Context Protocol by building MCP servers and clients that connect an AI app to tools, data and prompts, then deploying a remote server. For developers building agentic apps.
View courseClaude Code: A Highly Agentic Coding Assistant
Practise using Claude Code to explore codebases, add features, write tests, fix bugs and manage work across several sessions. Aimed at developers who want to code faster with an AI assistant.
View courseReasoning with o1
Learn how OpenAI reasoning models differ from chat models, how to prompt them well, and how to use them for planning, coding and meta-prompting tasks. Suits developers already using LLM APIs.
View courseKnowledge Graphs for RAG
Learn how knowledge graphs store connected data and how to query them with Cypher to give LLM apps richer context. Suits developers who want to go beyond plain vector search.
View coursePretraining LLMs
Covers the steps of pretraining a language model: preparing data, configuring model architecture, training and evaluation, and when pretraining makes sense. For learners with machine learning basics.
View courseReinforcement Learning From Human Feedback
Understand how RLHF aligns language models with human preferences and walk through tuning and evaluating a model with preference data. Good for learners interested in model alignment.
View courseLong-Term Agentic Memory With LangGraph
Build an email assistant agent that learns over time using semantic, episodic and procedural memory in LangGraph. For developers who want agents that remember user preferences.
View courseHugging Face LLM Course
Learn natural language processing and large language models with the Transformers, Datasets and Tokenizers libraries, from using pretrained models to fine-tuning and sharing them. Suits Python users with some ML basics.
View courseHugging Face AI Agents Course
Study how AI agents think, use tools and act, then build agents with frameworks like smolagents, LlamaIndex and LangGraph, finishing with a final project. For developers comfortable with Python.
View courseHugging Face Audio Course
Apply transformers to audio tasks such as speech recognition, audio classification and text-to-speech, working with real audio data. Good for developers who know Python and basic machine learning.
View courseHugging Face Deep Reinforcement Learning Course
Learn deep reinforcement learning from theory to practice, training agents in game and simulation environments with popular libraries. Suits learners who want hands-on RL projects and have some Python skills.
View courseHugging Face Model Context Protocol (MCP) Course
Understand the Model Context Protocol and build MCP servers and clients that link language models to external tools and data, with practical projects. For developers building AI integrations.
View courseHugging Face Community Computer Vision Course
A community-built course covering computer vision foundations, CNNs, vision transformers, generative models and multimodal models with Hugging Face tools. Useful for learners who want broad vision knowledge.
View courseHugging Face Machine Learning for 3D Course
Explore how generative AI creates 3D content, covering 3D representations and building a text-or-image-to-3D pipeline with open tools. Suits learners interested in 3D graphics and AI.
View courseHugging Face smol-course
A practical course on aligning and fine-tuning small language models for your own tasks, with exercises that run on modest hardware. Good for developers wanting hands-on post-training experience.
View courseHugging Face Robotics Course
Learn how machine learning is applied to robotics, from classical approaches to learning-based robot control using open-source tools. For learners curious about AI in physical systems.
View coursePractical Deep Learning for Coders
A hands-on deep learning course for people who can code, teaching you to train models for vision, text and tabular data with PyTorch and fastai before digging into how they work.
View courseIntermediate Machine Learning
Improve model quality by handling missing values and categorical data, building pipelines, using cross-validation and applying XGBoost, while avoiding data leakage. For learners who finished the intro course.
View courseFeature Engineering
Learn techniques for creating and selecting better model inputs, including mutual information, clustering features, PCA and target encoding. Suits learners who already build basic ML models.
View courseAdvanced SQL
Go beyond basic queries with joins and unions, analytic window functions, nested data and writing efficient queries in BigQuery. For learners who have finished Intro to SQL.
View courseIntro to Deep Learning
Build and train neural networks with TensorFlow and Keras, learning about layers, stochastic gradient descent, overfitting, dropout and classification. Good for learners with basic ML knowledge.
View courseComputer Vision
Learn how convolutional neural networks classify images, covering convolution, pooling, custom convnets, data augmentation and transfer learning with Keras. Suits learners who finished Intro to Deep Learning.
View courseTime Series
Learn to model trends, seasonality and cycles in time-based data and build forecasting models with machine learning. Useful for analysts working with sales, finance or sensor data.
View courseMachine Learning Explainability
Learn methods such as permutation importance, partial dependence plots and SHAP values to understand what drives a model's predictions. Suits practitioners who need to explain models to others.
View courseGeospatial Analysis
Create interactive maps and analyse location data with GeoPandas and Folium, covering coordinate systems, geocoding and proximity analysis. Good for analysts working with maps or location data.
View course5-Day Gen AI Intensive Course with Google
A self-paced guide built with Google covering foundation models, prompt engineering, embeddings, vector stores, agents and MLOps for generative AI, with whitepapers, podcasts and code labs. Suits developers with Python skills.
View course5-Day AI Agents Intensive Course with Google
A self-paced programme with Google on building AI agents, covering tools, memory, evaluation and deploying multi-agent systems through readings and hands-on notebooks. For developers ready to build agents.
View courseBuilding AI
The follow-up to Elements of AI, introducing the algorithms behind AI such as optimisation, Bayesian reasoning, machine learning and neural networks, with optional Python exercises and a project idea.
View courseClaude Code in Action
Learn to use Claude Code in real development work, including giving it context, managing tools, running it through custom commands and hooks, and connecting MCP servers. For developers who code daily.
View courseBuilding with the Claude API
A full developer course on the Claude API, covering prompting, evaluation, tool use, retrieval, MCP and agent workflows. Suits programmers who want to build production features with Claude.
View courseIntroduction to Model Context Protocol
Build MCP servers and clients in Python, learning how tools, resources and prompts let an AI app connect to outside systems. Good for developers new to the protocol.
View courseClaude with Amazon Bedrock
Learn to build with Claude through Amazon Bedrock, including prompting, tool use, retrieval and agent patterns on AWS. Suits cloud developers working in the AWS ecosystem.
View courseClaude on Google Cloud
Learn to access and build with Claude models on Google Cloud Vertex AI, covering prompts, tools, retrieval and evaluation. Good for developers deploying AI on Google Cloud.
View courseCS50's Introduction to Artificial Intelligence with Python
Explore the ideas behind modern AI, including search, knowledge, uncertainty, optimisation, machine learning, neural networks and language, by building AI programs in Python. Best after CS50x or CS50P.
View courseCS50's Web Programming with Python and JavaScript
Learn to design and build web apps with Python, Django, JavaScript, SQL and Git, covering testing, security and scalability. Suited to learners who finished an intro programming course.
View courseCS50's Introduction to Game Development
Learn how classic 2D games are built by studying and recreating titles like Pong, Flappy Bird and Mario using Lua and the LOVE framework. For learners with some programming experience.
View courseArtificial Intelligence (6.034)
Patrick Winston's classic MIT course on how to build intelligent systems, covering search, constraints, rules, learning methods and neural networks, with full lecture videos. For learners with programming basics.
View courseIntroduction to Computational Thinking and Data Science (6.0002)
Follows 6.0001 with optimisation, graph problems, simulations, statistics and introductory machine learning in Python. Good for learners heading towards data science.
View courseIntroduction to Algorithms (6.006)
Covers core algorithms and data structures such as sorting, searching, hashing, graphs and dynamic programming, with lectures and problem sets. Essential preparation for technical interviews.
View courseLinear Algebra (18.06)
Gilbert Strang's well-known linear algebra lectures on matrices, vector spaces, determinants, eigenvalues and applications. Valuable maths background for machine learning and data science.
View courseIntroduction to Machine Learning (6.036)
MIT's introduction to machine learning, covering linear classifiers, regression, neural networks, convolutional networks, reinforcement learning and more. Suits learners with Python and basic maths.
View courseMathematics for Computer Science (6.042J)
Teaches the discrete mathematics used in computer science, including proofs, graph theory, counting and probability. Strong preparation for algorithms and theory courses.
View courseMIT Introduction to Deep Learning (6.S191)
MIT's fast-paced introduction to deep learning, with lectures and software labs on neural networks, computer vision, generative models and large language models. Suits learners with basic Python and calculus.
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