Turn Data Into
Your Superpower

Complete AI Engineer Bootcamp

Full-stack AI engineering: LLMs, agents, RAG, fine-tuning, and deployment — the practical path from AI user to AI builder.

Artificial Intelligence A-Z

Build intuition for how AI systems actually work — from optimization and deep Q-learning to neural networks and production AI.

AI Engineer 2026

End-to-end AI engineering for 2026: generative AI, deep learning, machine learning, and LLMs in a single structured track.

Generative AI for Beginners

Understand how LLMs generate text and images from the ground up — transformers, tokens, embeddings, and your first GenAI app.

Claude Code: The Practical Guide

Use Claude Code to go from prompt to working project — setup, agentic workflows, and real tasks done in the terminal.

AI Coder: Vibe Coder to Agentic Engineer

A 3-week track turning AI-assisted coding habits into disciplined agentic engineering — build systems that actually run.

Full Stack Generative & Agentic AI

End-to-end Python course: build generative AI apps and autonomous agent systems from scratch — no shortcuts.

Prompt Engineering & Generative AI

Build prompt systems that produce consistent, structured results — covering chain-of-thought, RAG, and production patterns.

AI Fundamentals for Beginners

How LLMs work, how to build agentic AI systems, and what the Model Context Protocol actually does.

Data Analysis with Polars & Python

Analyze real datasets in a faster, cleaner Python library — filtering, groupby, and joins without the pandas overhead.

Master SQL for Beginners

Write real SQL against actual databases — queries, joins, aggregations, and subqueries — before the first section ends.

Google Colab

Run your first data analysis — no install needed. Free Python notebooks in your browser.

Replit

Write and run Python or SQL instantly — no installs, no setup. A full coding environment in your browser.

Tableau Public

Turn your data into a story anyone can see. Build and publish interactive dashboards.

Data Modeling Fundamentals

Design databases that hold up under real use. Normalization, star schema, keys, indexes, and SCDs — with SQL exercises throughout.

Prompt Engineering for Data Analysis

Stop guessing at prompt structure. Write analytical workflows that return consistent, structured results — chain-of-thought, role prompting, and multi-step patterns in Python.

LLMs for Feature Engineering

Stop engineering features by hand. Use LLMs to extract signal from raw text, label messy data, and validate what you built — all in a Python notebook.

ELT Explained

Finally understand what happens between your raw data and your analytics tables — extraction, loading, and transformation, no code required.

SQL Fundamentals

Go from reading SQL to writing it. Queries, joins, aggregations, and window functions — through exercises, not slides.

Python Fundamentals

Write Python that actually does something. Data types, control flow, functions, and real data handling — through exercises, not reading.