Step 1: Understand the Basics
Start with the fundamental concepts. You don't need to understand the math, but you should understand what these things are and why they matter:
Machine Learning: How computers learn from data instead of being explicitly programmed. This is the foundation of everything.
Neural Networks: The computing structures (loosely inspired by brains) that power modern AI. Understanding layers, weights, and training at a high level makes everything else click.
Large Language Models: The technology behind ChatGPT and Claude. Understanding how they work (next-token prediction) helps you use them better and understand their limitations.
Read those three guides and you'll have a solid foundation. Everything else builds on these concepts.
Step 2: Use AI Tools
The best way to learn is by doing. Start using AI tools in your daily work:
ChatGPT or Claude: Use them for writing, research, brainstorming, data analysis, and coding help. The more you use them, the better you'll get at prompting.
Image generation: Try Midjourney, DALL-E, or Stable Diffusion. Understanding how they work helps you get better results.
Coding assistants: GitHub Copilot, Cursor, or Codeium. Even if you're not a developer, seeing AI write code makes the technology tangible.
Specialized tools: Perplexity for research, Notion AI for note-taking, Descript for audio/video editing. AI is embedded in tools across every category.
Step 3: Go Deeper (Pick Your Path)
Once you have the basics, choose what interests you most:
If you want to use AI better: Study prompt engineering, learn about RAG, and understand AI agents. These are the practical skills that let you get more from AI tools.
If you want to build with AI: Learn about fine-tuning, embeddings, and the API ecosystem. You can build AI-powered applications without training models from scratch.
If you want to understand the technology: Study deep learning, transformers, and how models are trained. This is the technical path.
If you care about impact: Focus on AI safety, ethics, and regulation. These are the questions that determine whether AI ends up being good for humanity.
Step 4: Stay Current
AI moves fast. What's cutting edge today might be old news in six months. A few ways to stay up to date:
Follow the research: arXiv papers, Google Scholar alerts, and AI research labs' blogs. You don't need to read every paper — read the abstracts and follow discussions on Twitter/X.
News sources: Machine Brief (hey, that's us), The Verge AI section, MIT Technology Review, and Ars Technica provide accessible coverage.
Podcasts: Lex Fridman, Practical AI, Gradient Dissent, and The AI Podcast cover the field from different angles.
Communities: r/MachineLearning, Hugging Face forums, AI Discord servers, and local meetups connect you with others learning and building.
Free Learning Resources
fast.ai: Practical deep learning courses. Top-down approach — you build things first, then learn theory.
Andrej Karpathy's YouTube: Former OpenAI researcher explaining neural networks, GPT, and tokenization from scratch. Brilliant content.
3Blue1Brown: Visual math explanations. Their neural networks series makes the math intuitive.
Hugging Face courses: Free courses on NLP, transformers, and practical ML. Hands-on with real code.
Google's Machine Learning Crash Course: Structured introduction to ML fundamentals.
Your Reading Order
If you're starting from zero, read these guides in this order:
- Machine Learning — the foundation
- Neural Networks — the building blocks
- Large Language Models — what powers ChatGPT
- Prompt Engineering — using AI effectively
- Transformers — the architecture behind it all
Then explore whatever interests you most from our full learning library.