What is deep learning? Learning representations through layers
Learn what deep learning means, how layered neural networks train, where they help and what to study before building your first deep learning project.
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AI and machine learning guides
Direct answers to common questions about AI, machine learning and how to learn them. Each guide starts with the short answer, then explains how it works, with examples and sources.
All free resourcesAI applications by fieldArticlesCheat sheetsAI glossary
AI subjects explained
What each field of AI is, how it works and what to learn before you study it.
What is computer vision? Turning images into useful information
Understand computer vision, image classification, object detection and segmentation, with a beginner project plan and a path through Python and ML foundations.
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What is a neural network? From inputs to a trainable prediction
Learn how a neural network makes predictions using weights, biases and activations, how training works, and which maths and Python skills to learn first.
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What is natural language processing? Working with human language
Understand natural language processing, tokenisation, embeddings and language models, with a text classification project plan and beginner learning path.
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What is generative AI? Creating content from learned patterns
Learn what generative AI does, how it differs from prediction and search, and how prompting, retrieval and fine-tuning fit into a practical learning path.
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Supervised vs unsupervised learning: start with the learning signal
Compare supervised and unsupervised learning with labelled-data examples, classification, regression, clustering and practical evaluation advice for beginners.
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What is reinforcement learning? Learning actions from their consequences
Learn reinforcement learning through agents, actions, rewards and policies, with a small simulation example and the Python, statistics and ML prerequisites.
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Choosing what and how to learn
Courses compared, learning paths and projects, so you can choose by your starting point.
The best machine learning course for your starting point
The best machine learning course depends on where you start. Seven compared on cost, prerequisites and format, from Coursera and Google to fast.ai and MIT.
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What is machine learning? Learning a rule from examples
Learn what machine learning is, how training differs from ordinary programming, and the basic workflow from data and baseline to evaluation.
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Data science course: learn the question before the model
A data science course learning path, and whether you are ready for one: readiness checkpoints, formal eligibility and a one-afternoon diagnostic.
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Data science projects: make the decision and evidence visible
Build data science projects with a clear decision, baseline, evaluation plan and honest limitations instead of another unexplained notebook.
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Deep learning course: foundations before architectures
A deep learning course roadmap: the foundations to learn first, a tutorial study order, how to debug training, and where agentic AI fits.
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What is AI? A broad field, not one tool
Learn what artificial intelligence means, how machine learning and language models fit inside it, and where current systems need human judgment.
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