Best AI & machine learning Courses - Page 2
Hand-picked AI and tech courses, free and paid, that are worth your time.
120 courses · page 2 of 3
5-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 courseElements of AI
A friendly, non-technical introduction to what AI is, what it can and cannot do, and how it affects society, with no maths or coding required. Ideal for students and professionals from any field.
View courseEthics of AI
Examines the ethical questions raised by AI, including fairness, transparency, accountability, privacy and human rights, using real examples. Suits anyone who wants to think critically about responsible AI use.
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 courseModel Context Protocol: Advanced Topics
Goes deeper into MCP with sampling, notifications, roots, transports and running servers in production. For developers who already built a basic MCP server and want to scale it.
View courseAI Fluency: Framework & Foundations
Learn a practical framework for working with AI effectively and responsibly, covering delegation, clear description, critical evaluation and diligence. Useful for professionals in any role.
View courseAI Fluency for educators
Helps teachers and faculty apply AI fluency ideas to lesson planning, course design and their own teaching practice in a thoughtful way. Suited to educators at schools and universities.
View courseAI Fluency for students
Shows students how to use AI to support learning, career planning and academic work while keeping their own thinking and integrity. Ideal for university and college students.
View courseTeaching AI Fluency
Guides educators in teaching and assessing AI fluency with their students, with ready-to-adapt ideas for classroom activities. Useful for teachers designing AI literacy lessons.
View courseIntroduction to agent skills
Learn what agent skills are and how to create, organise and share them so Claude can follow repeatable workflows. Good for developers and power users customising AI tools.
View courseAI Capabilities and Limitations
Explains what current AI models do well, where they fall short and why, so you can decide when to trust their output. Useful for anyone using AI tools at work or study.
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 courseMatrix Methods in Data Analysis, Signal Processing, and Machine Learning (18.065)
Gilbert Strang explains the linear algebra behind deep learning and data analysis, including SVD, low-rank approximation, optimisation and gradient descent. For learners with linear algebra basics.
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 courseCS229: Machine Learning
Stanford's core machine learning course covering supervised and unsupervised learning, learning theory and reinforcement learning, with notes and lecture materials. Best for learners with strong maths and programming skills.
View courseCS231n: Deep Learning for Computer Vision
A detailed look at deep learning for vision, from building neural networks and CNNs to detection, segmentation and generative models, with notes and assignments. For learners comfortable with Python and linear algebra.
View courseCS221: Artificial Intelligence: Principles and Techniques
Stanford's broad AI course covering machine learning, search, Markov decision processes, games, Bayesian networks and logic, with lecture materials and assignments. Suits learners with programming and maths foundations.
View courseMachine Learning Specialization
Andrew Ng's beginner-friendly three-course series on regression, classification, neural networks, decision trees, recommender systems and reinforcement learning using Python. Ideal first step for aspiring ML engineers.
View courseAI Fluency
A friendly starting point for anyone new to AI: understand what today's AI tools can do, how to use them responsibly at work, and how to get useful results from chat assistants.
View courseAI concepts for developers and technology professionals
Covers the core ideas behind machine learning, generative AI, computer vision and language models, giving developers and IT learners the vocabulary needed before building AI solutions on Azure.
View courseGet started with AI applications and agents on Azure
Hands-on introduction to building AI apps and simple agents with Azure services, suited to students and junior developers who want practical experience beyond theory.
View courseIntroduction to AI literacy
Builds everyday AI literacy: how AI systems work, where they fail, and how to judge their output critically. Good for students, teachers and non-technical job seekers.
View courseGitHub Copilot Fundamentals Part 1 of 2
Shows how to use GitHub Copilot as an AI pair programmer in your editor, write effective prompts for code, and apply it responsibly. Aimed at students and working developers.
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 courseIntroduction to Generative AI
A short microlearning course explaining what generative AI is, how it differs from traditional machine learning, and which Google tools support it. Ideal first step for complete beginners.
View courseIntroduction to Large Language Models
Learn what large language models are, where they are used, and how prompt tuning improves their output. A quick, beginner-friendly overview for students and professionals.
View courseIntroduction to Image Generation
Explains how diffusion models create images from noise and how they are trained and deployed on Google Cloud. Good for learners curious about AI image tools.
View courseResponsible AI: Applying AI Principles with Google Cloud
Explore how organisations put responsible AI principles into practice, with real examples of balancing innovation, fairness and risk. Suits managers, students and AI practitioners.
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 courseGen AI: Beyond the Chatbot
First course of Google's Gen AI Leader path: see how generative AI goes beyond chat to transform business work, with no technical background required.
View courseGen AI: Unlock Foundational Concepts
Learn the key ideas behind generative AI, including data, models and machine learning approaches, in plain business language. Second course in the Gen AI Leader path.
View courseGen AI: Navigate the Landscape
Map out the generative AI ecosystem, from infrastructure and models to platforms and agents, and how organisations choose among them. Third course of the Gen AI Leader path.
View courseGen AI Apps: Transform Your Work
Discover Google's generative AI apps such as Gemini for Workspace and learn practical prompting techniques to speed up everyday work. Fourth course in the Gen AI Leader path.
View courseGen AI Agents: Transform Your Organization
Understand AI agents, how they automate business processes, and how organisations can adopt them responsibly. Final course of Google's Gen AI Leader learning path.
View courseInnovating with Google Cloud Artificial Intelligence
Business-level introduction to AI and machine learning on Google Cloud, including how to pick the right AI solution for a problem. Part of Cloud Digital Leader preparation.
View courseMachine Learning Crash Course
Google's well-known self-paced ML course with videos, visualisations and exercises covering regression, classification, neural networks and LLMs. Great for students and developers starting machine learning.
View courseIntroduction to Machine Learning
A brief, non-mathematical overview of what machine learning is, the main model types and how they learn. Good preparation before tackling the Machine Learning Crash Course.
View courseIntroduction to Machine Learning Problem Framing
Learn how to decide whether a problem needs machine learning and how to define goals, data and success metrics before building. Useful for developers and product-minded learners.
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 courseGetting Started with Generative AI
Learn how generative AI works, where it is useful, and how to use it responsibly, earning an IBM digital credential on completion. Designed for complete beginners, including students and job seekers.
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