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Paid conferences and bootcamps teach the topics that matter for AI engineers. The knowledge itself is scattered across free videos, docs, papers and tutorials. We assemble it into structured courses you can finish in order, for free.
Courses
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AI Foundations for IT Professionals
Start here if you work in IT and have no AI background. Learn what AI actually is, how machines learn, how neural networks and language models work, where they fail, and how to read the landscape, with no coding required.
Working with LLMs
Learn to get reliable, useful results from AI assistants. Choose the right assistant and mode, write clear prompts, use proven patterns, work with documents, use search and reasoning tools, and check answers and protect your data, with no coding required.
Python and Data Essentials
Learn just enough Python to start building with AI. Set up a clean environment, write and read scripts, work with files and JSON, call APIs safely, analyze data with pandas, use notebooks, and get help from an AI assistant without losing control, with examples from IT work.
Machine Learning Basics
Learn how machine learning models are built, tested and judged. Frame a problem, prepare data, train classic models with scikit-learn, evaluate them honestly, avoid overfitting and leakage, and know when to use clustering, anomaly detection, a rule or a language model.
Build with LLM APIs
Call a language model from your own Python code and build features that hold up. Make your first API call, manage conversations and streaming, get validated structured output, give the model tools safely, handle errors and cost, and test and secure an LLM feature.
Implement GraphRAG with Knowledge Graphs
Learn why plain vector RAG breaks on multi-hop and corpus-wide questions, and how knowledge graphs fix it. Build a graph from unstructured text, retrieve over it, and evaluate the result.
Engineer Persistent Memory for AI Agents
Agents are stateless by default. Learn how to give them short-term and long-term memory that stays accurate, affordable and safe: what to store, how to retrieve it, and how to prove it works.
Deploy Reliable AI Agents in Production
A demo that works once is not a product. Learn to measure agent reliability, trace and debug runs, survive failures, defend against prompt injection, control cost and ship changes safely.
Engineer Repository Context for Coding Agents
Coding agents are only as good as what they can see. Learn to write instruction files, make code easy to find, build fast feedback loops, package skills and subagents, and measure whether any of it helps.
Implement Browser Automation for AI Agents
Give agents the ability to use the web. Learn how browsers are automated, how to show a page to a model, how to design the agent loop, and how to make runs reliable, safe and respectful.
Engineer Synthetic Data for Open LLMs
Open models need good data. Learn to generate instruction, preference and pretraining-style data with LLMs, filter and decontaminate it, train small models on it, and prove with real held-out data that it helped.
Explore Meta-Harness Architectures for AI Agents
The harness around a model often matters as much as the model. Learn what a harness is, how long-running harnesses are built, what "meta-harness" means, and how to search over harness code with your own data.
Architect Agentic Systems for Financial Workflows
Design systems where language models help with financial workflows without being trusted with what they should not decide: exact money handling, validated extraction, approvals, audit trails, evaluation and safe rollout. Built on synthetic data only.
Analyze AI-Generated Code for Quality and Security
AI tools write plausible code that can be wrong, insecure or built on packages that do not exist. Learn to review it by risk, recognize common weaknesses, vet dependencies, automate analysis and test it properly.
More courses coming,
one topic at a time