Best AI & machine learning Courses
Hand-picked AI and tech courses, free and paid, that are worth your time.
120 courses · page 1 of 3
Building Systems with the ChatGPT API
Shows how to chain several LLM calls into a working customer-service style system, covering classification, moderation, chain-of-thought and output evaluation. Suits Python developers moving past single prompts.
View courseLangChain for LLM Application Development
An introduction to the LangChain framework: models, prompts, memory, chains, question answering over documents and simple agents. Useful for developers who want to build LLM apps quickly in Python.
View courseLangChain Chat with Your Data
Build a chatbot that answers questions from your own PDFs and web pages using document loading, splitting, embeddings, vector stores and retrieval. A solid first step into retrieval augmented generation.
View courseFinetuning 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 courseBuilding Generative AI Applications with Gradio
Create quick web demos for text summarisation, image captioning, image generation and chat using Gradio and open models. Handy for anyone who wants to showcase AI projects in a portfolio.
View courseLarge Language Models with Semantic Search
Learn how embeddings, dense retrieval and reranking improve keyword search, and how to combine search results with an LLM to answer questions. Suits developers building search or RAG features.
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 courseBuilding and Evaluating Advanced RAG
Learn advanced retrieval methods such as sentence-window and auto-merging retrieval, and how to measure RAG quality with evaluation metrics. Useful for teams putting document question answering into production.
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 courseMulti AI Agent Systems with crewAI
Design teams of role-based AI agents that collaborate on business tasks like research, customer support and content planning using the open-source crewAI framework. Beginner friendly for Python users.
View courseBuilding Agentic RAG with LlamaIndex
Build a research agent that routes queries, calls tools and reasons over multiple documents with LlamaIndex. A compact course for developers who want smarter document assistants.
View courseQuantization Fundamentals with Hugging Face
Learn how to shrink models with linear quantization and lower-precision data types so they run on smaller hardware, using Hugging Face tools. Suits learners curious about efficient model deployment.
View courseOpen Source Models with Hugging Face
Find and use open models from the Hugging Face Hub for text, audio, image and multimodal tasks, then share demos with Gradio and Spaces. Great for beginners building quick AI prototypes.
View courseBuilding toward Computer Use with Anthropic
Work through the Claude API features behind computer use: multimodal prompts, tool use, prompt caching and an agent that controls a desktop. Good for developers exploring AI agents.
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 courseAttention in Transformers: Concepts and Code in PyTorch
Understand the attention mechanism behind transformers and code self-attention, masked attention and multi-head attention in PyTorch. Good for learners who want to see how LLMs work inside.
View courseHow Transformer LLMs Work
A visual walk through tokenizers, embeddings, attention and the transformer block, explaining how modern language models generate text. Suits anyone who wants a clear mental model of LLMs.
View courseEvaluating AI Agents
Learn to trace, test and score AI agents, measuring each step such as routing and tool calls so you can improve reliability systematically. Useful for developers shipping agent-based products.
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 courseGetting Started with Mistral
Explore Mistral's open and commercial models, using them for prompting, function calling, JSON output and RAG through the API. A quick start for developers comparing LLM providers.
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 courseEmbedding Models: from Architecture to Implementation
Learn how embedding models evolved from word vectors to transformer-based encoders and how to train a dual encoder for semantic search. Good background for anyone building retrieval systems.
View courseIntroduction to on-device AI
Learn how to optimise and deploy AI models on phones and edge devices, covering model conversion, quantization and on-device testing. Suits developers interested in mobile and embedded AI.
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 Diffusion Models Course
Learn the theory behind diffusion models, train your own from scratch, fine-tune existing ones and generate images with the Diffusers library. Best for learners with deep learning experience.
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 Games Course
See how AI tools can be used in game development, for example to create NPCs and game assets, with practical demos. A good fit for game developers and hobbyists curious about ML.
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 coursePractical Deep Learning for Coders - Part 2: From Deep Learning Foundations to Stable Diffusion
The advanced follow-up to fast.ai Part 1, rebuilding deep learning foundations and implementing Stable Diffusion style models from scratch. Suited to experienced learners who want deep technical understanding.
View coursePractical Data Ethics
Covers ethical issues in data science and AI such as disinformation, bias, privacy, surveillance and algorithmic accountability, with readings and lectures. Useful for anyone building or deploying data products.
View courseIntro to Machine Learning
Build your first machine learning models, learning about decision trees, random forests, validation and overfitting using real datasets. Suits beginners who know a little Python.
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 courseIntro to Game AI and Reinforcement Learning
Build game-playing agents, starting with simple heuristics and minimax search and moving on to reinforcement learning. A fun entry point for learners curious about how AI plays games.
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 courseIntro to AI Ethics
Explore practical tools for building fairer, more responsible AI, including human-centred design, identifying bias, fairness metrics and model cards. Useful for anyone who builds or manages AI systems.
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.
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