When three minutes isn't enough.

Google's own ML primer with interactive exercises. The fastest credible route from "no idea" to "I can read a model card".

LLMs, embeddings, responsible AI and Vertex. Feeds directly into the Gen AI Leader exam.

Builds a transformer from scratch on video. Hard, worth it, and the best cure for treating models as magic.

If backpropagation has never clicked, watch this before reading another explainer.

Practical and code-first. Fine-tuning, tokenizers, datasets - for PMs who want to run something, not just discuss it.

This article looks at where foundation models fall short and how retrieval-augmented generation (RAG) fixes those gaps for chat, search, and agentic workflows.

The reference I keep open. Techniques, failure modes and papers, kept current - better than any paid prompt course.

Built with the model vendors. The fastest way to stay current on a specific technique.

Top-down teaching - you train a model in lesson one. Needs real Python.

Short notebook-based micro-courses. Intro to ML and Feature Engineering are the useful two.

Vendor material that is unusually candid about limitations. Good on prompting and agent design.

Introductory and applied tracks. Useful for bringing a non-technical team up to a shared vocabulary.

A structured twelve-week curriculum on GitHub. Dry, thorough, and genuinely complete.

The closest thing to a canon for empowered product teams. Start with "Product vs. Feature Teams" and work backwards.

Deep operator interviews and benchmarks. The free archive alone beats most paid courses.

This YouTube tutorial from Microsoft Developers is very helpful for beginners who want to learn about MCP.

Real PRDs, strategy docs and launch plans from real companies. Steal the structure, not the content.

Short, practical playbooks on agile ceremonies and team workflows - sprints, retros, roadmapping formats - no signup required and no fluff.

Conference talks and articles. Good for breadth and for spotting what's current.

The foundations of product management matter most. I've found these two books especially helpful for building a strong PM career.

The threat list every AI PM should be able to recite. Prompt injection, data leakage, supply chain, excessive agency.

Want to learn more about AI security? This learning path helps you understand the basic concepts of AI security.