Zero to Elite
AI Engineer
10courses. 4 phases. One path — follow it in order and you'll go from fundamentals to building production AI systems.
Foundation
The non-negotiables every engineer needs before anything else.
Git Fundamentals — Version Control for Every Developer
Master Git from the ground up — commits, branches, merges, rebases, and collaboration patterns used by professional engineering teams. Practical exercises at every step.
GitHub for Developers — Collaboration, CI/CD & Open Source
From creating your first repository to automating workflows with GitHub Actions — learn how professional teams collaborate on GitHub. Covers pull requests, code review, issues, project boards, secrets management, and CI/CD pipelines.
Python Mastery — From Zero to AI Engineering
A complete Python course built for developers who want to reach expert level. Starts from first principles, builds through data structures, OOP, and advanced patterns, then dives into NumPy, Pandas, scikit-learn, PyTorch, and production AI application development. Every lesson includes runnable code directly in the browser.
AI Foundations
Understand how LLMs work and how to work with them at expert level.
Introduction to Large Language Models
A hands-on course for engineers who want to understand how LLMs work under the hood and build real applications with them.
Claude Code Superpowers: AI That Gets Smarter With Every Task
Turn Claude Code from a reactive assistant into a proactive engineering partner. Learn to install and wield the Superpowers skill system — discipline protocols, domain intelligence, persistent memory, and multi-agent coordination — so your AI compounds knowledge instead of starting from scratch every session.
Data & Science
The data intuition and statistical thinking that separates good engineers from great ones.
Data Analysis with Python — Expert Practitioner Track
A practitioner-level course covering everything a world-class data analyst does: project scoping, data quality, cleaning, wrangling, EDA, feature engineering, statistical analysis, advanced visualisation, insight generation, and professional reporting.
Data Science Fundamentals — From Theory to Production Models
Master the mathematical, statistical, and algorithmic foundations that every production data scientist depends on. Covers probability, distributions, regression, classification, clustering, model evaluation, explainability, and end-to-end pipelines.
Production Engineering
Build systems that ship — agents, RAG pipelines, and full ML infrastructure.
AI Automation — Production Agents & Agentic Systems
Build production AI agents from first principles: agent architectures, tool use, multi-step planning, multi-agent systems, memory, guardrails, observability, and enterprise deployment. Go beyond demos to systems that reliably automate real work.
RAG Engineering — Production Retrieval-Augmented Generation
Design, build, and ship production RAG systems from first principles: document processing, chunking strategies, embedding models, vector databases, retrieval quality, query transformations, reranking, evaluation, advanced architectures, and production operations.
Machine Learning Engineering — Production ML Systems
Build ML systems that actually ship: data pipelines, training infrastructure, experiment tracking, model packaging, inference serving, monitoring, and CI/CD for ML. Everything a senior MLE needs to take a model from notebook to production.
Start at Phase I
Don't skip ahead. Each phase builds on the last. Engineers who follow the path complete it — engineers who jump ahead stall out.
Begin with Git Fundamentals