Courses, guides, and learning paths to take you from curious to capable — hand-picked for quality.
Vetted courses from top universities and AI labs — most are free to audit.
A broad introduction to what AI is, what it can and can't do, and how it affects society. No math required.
Andrew Ng's non-technical course covering what AI can do, how to work with AI teams, and AI strategy.
Practical introduction to using generative AI tools at work — prompting, productivity, and responsible use.
Concepts behind machine learning, computer vision, NLP, and generative AI. Prepares for the AI-900 cert.
Hands-on deep learning using PyTorch. Covers vision, NLP, tabular data, and deployment. Top-down teaching style.
Learn to use transformers for NLP tasks: classification, summarisation, Q&A, and fine-tuning on custom data.
Covers supervised/unsupervised learning, regression, neural networks, decision trees, and recommender systems.
CNNs, RNNs, sequence models, optimisation. Five rigorous courses building up to applied deep learning projects.
Andrej Karpathy builds language models from scratch — backprop, character-level LMs, GPT, and more.
Word vectors, attention, transformers, pretraining, and advanced NLP systems. Full lecture videos available.
MIT's official intro to deep learning — covers foundations, CNNs, RNNs, reinforcement learning, and generative models.
Production LLM systems — fine-tuning, RAG, evaluation, deployment, and monitoring. From Databricks & deeplearning.ai.
Curated sequences depending on what you want to achieve with AI.
For professionals who want to integrate AI tools into their daily workflow — no coding required.
For developers who want to build products using AI APIs, fine-tuning, and deployment pipelines.
For those aiming to understand, replicate, or advance the state of the art in machine learning.
Structured resources on writing effective prompts for ChatGPT, Claude, Gemini, and other LLMs.
The most comprehensive free guide to prompt engineering — chain-of-thought, few-shot, zero-shot, and advanced techniques with examples.
Six strategies for better results: clear instructions, reference text, decomposing complex tasks, giving the model time to think.
Claude-specific techniques covering role prompting, XML tags for structure, chain-of-thought, and how to avoid common failures.
Research-backed guide from DAIR.AI covering CoT, ReAct, SELF-CONSISTENCY, and prompting for code, reasoning, and retrieval.
Step-by-step guides to get the most out of the leading AI platforms.
Conversation, writing, coding, image generation, and custom GPTs.
View guide →Long documents, research, coding, and analysis. System prompts and projects.
View guide →AI-powered search with citations. Best for research and fact-checking.
View guide →Text-to-image generation — parameters, style references, aspect ratios.
View guide →AI pair programming in VS Code, JetBrains, and the CLI.
View guide →AI-native code editor. Agent mode, codebase context, custom rules.
View guide →AI writing, summarisation, and Q&A across your workspace.
View guide →Open-source model hub, Transformers library, Spaces, and Inference API.
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