Best AI & machine learning Courses
Hand-picked AI and tech courses, free and paid, that are worth your time.
46 courses
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 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 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 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 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 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 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 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 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 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 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.
View courseDevelop generative AI apps on Microsoft Foundry
For developers ready to build: create generative AI applications with Microsoft Foundry, including prompt flows, retrieval-augmented generation and model evaluation. Requires some programming experience.
View courseAttention Mechanism
Understand the attention mechanism that lets neural networks focus on relevant input, and how it powers translation and summarisation. Best for learners with some machine learning background.
View courseTransformer Models and BERT Model
Covers the transformer architecture and the BERT model, including self-attention and typical NLP uses such as text classification. Suited to learners with basic ML knowledge.
View courseEncoder-Decoder Architecture
Learn how encoder-decoder networks handle sequence tasks like translation, with a walkthrough of training and serving a simple model. Aimed at learners comfortable with ML basics.
View courseClustering
Explains unsupervised clustering techniques such as k-means, how to measure similarity, and how to judge cluster quality. Suits learners who already know basic machine learning.
View courseRecommendation Systems
Learn how recommendation engines generate, score and rank suggestions using techniques like collaborative filtering and embeddings. Best for learners with prior ML experience.
View courseDecision Forests
Covers decision trees, random forests and gradient boosted trees, with practical guidance on training them for tabular data. Suited to learners comfortable with ML fundamentals.
View courseAI Fundamentals: Foundations for Understanding AI
Build a solid grasp of AI history, machine learning concepts and ethical questions, with an IBM digital credential at the end. Suitable for learners wanting deeper AI understanding.
View courseAI Fundamentals: Language and Vision in AI
Explore how AI understands text and images through natural language processing and computer vision, and earn an IBM credential. Good follow-on for learners who know AI basics.
View courseBuilding Generative AI Applications Using Amazon Bedrock
Learn to design and build generative AI applications with Amazon Bedrock, including architecture patterns and use cases like summarisation and chatbots. Requires some developer experience.
View courseMachine Learning with Python
Covers neural networks, reinforcement learning and core machine learning algorithms using TensorFlow, with projects such as image and text classifiers. Best for learners comfortable with Python who want to enter AI.
View courseLearn RAG and MCP Fundamentals
Introduces Retrieval-Augmented Generation and the Model Context Protocol, showing how AI agents fetch outside knowledge and use tools. Suits developers who want to build practical LLM-powered applications.
View courseMongoDB Atlas Vector Search Fundamentals
Introduces vector embeddings and how to run semantic similarity search in MongoDB Atlas, earning a skill badge. Suits developers starting to build AI-powered search features.
View courseRetrieval Augmented Generation (RAG) with MongoDB
Teaches building a RAG application that feeds relevant data from MongoDB into a large language model for grounded answers. For developers creating chatbots and AI assistants.
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