AI Bootcamp

The "Get Shit Done with AI" Bootcamp focuses on real-world applications that will equip you with the skills and knowledge to become a great AI engineer.

• Join the Discord community
• Watch the YouTube channel
• Join the AI Engineering Academy

AI/ML Foundations

Master the core code and concepts, from Python essentials to your first powerful machine learning model

Lesson	Description	Tutorial	Video
Python Essentials for AI Engineering	Master Python data structures, functional tricks, typing, JSON, pathlib, NumPy, and Pandas - all distilled for machine-learning engineers.	Read
Mathematics is the Language of AI	Build rock-solid intuition for AI: grasp the essentials of linear algebra, calculus, and probability through hands-on Python examples and practical engineering tips	Read
Start Simple - The Power of Linear Models	Learn how and why to build strong, interpretable baselines: explore linear regression end-to-end, from feature scaling to evaluation, with hands-on notebooks and real data.	Read
Essential PyTorch for Real-World Applications	Hands-on PyTorch fundamentals: tensors, autograd, data loading, optimizers, and full training loops - everything you need to build and deploy deep-learning models in production.	Read

MLOps and Production Systems

Don't just build models - ship them. Master the production lifecycle from data pipelines to live API deployment.

Lesson	Description	Tutorial	Video
Understanding Your Data - Data Exploration	Master data exploration for AI production. Analyze the Bank Marketing dataset using Pandas/Seaborn to understand distributions, find issues (missing data, outliers), and inform reliable data validation & preprocessing pipelines.	Read
Fueling Production AI - Data Validation & Pipelines	Master robust data pipelines: validate raw data with pandera, engineer features with scikit-learn Pipelines, and version everything with DVC for reliable ML in production.	Read
Reproducible Training - ML Pipelines & Experiment Tracking	Discover reproducible ML training: build DVC-driven pipelines, track experiments with MLflow, and tune LightGBM models for real-world impact in this hands-on tutorial.	Read
From Model to Service - Building and Dockerizing APIs	Take your trained machine learning model and build a production-ready REST API using FastAPI. Then, learn to package your application and all its dependencies into a portable Docker container.	Read
Serving at Scale - Cloud Deployment with AWS	Learn to deploy a containerized ML model to the cloud. This guide covers pushing artifacts to S3, storing your Docker image in ECR, and orchestrating deployment with AWS ECS and EC2.	Read

AI Systems Engineering

Master the full-stack toolkit for building cutting-edge applications on top of Large Language Models.

Lesson	Description	Tutorial	Video
Run AI Models Locally - Ollama Quickstart	Get started with local AI development. Learn to install and use Ollama to run powerful AI models on your own machine for enhanced privacy, speed, and cost-efficiency.	Read	Watch
Prompt Engineering	Learn how to write effective prompts for AI models using a battle-tested template	Read
The AI Engineer Toolkit - APIs, structured output, tools	Learn to use APIs, structured output, and tools to enhance your LLMs applications	Read	Watch
LangChain Foundations - An Engineer's Guide	Master the essentials of LangChain, the go-to framework for building robust LLM applications. Learn to manage prompts, enforce structured outputs with Pydantic, and build a simple RAG pipeline to chat with your documents.	Read	Watch
Connect AI to External Systems - Model Context Protocol	Learn to connect AI/LLMs to external systems using the Model Context Protocol (MCP). This hands-on tutorial guides AI engineers through building MCP servers and clients with Python, Ollama, and Streamlit, solving complex integration challenges with a standardized approach. Build a practical todo list agent.	Read	Watch
Build Your Own Dataset with Knowledge Distillation	Use a powerful LLM as a 'teacher' to automatically label raw data and create custom datasets for training and evaluating specialized models.	Read	Watch
No More Manual Tweaking - Automated Prompt Engineering	Learn to use DSPy to automatically optimize your prompts, turning a mediocre baseline into a high-performing pipeline. Use a powerful 'prompt model' to teach a smaller, faster 'task model' how to excel at financial sentiment analysis.	Read	Watch
Lies, Damn Lies and Hallucinations - Evaluating your LLMs	How do you know if your LLM is good? Evaluating your LLMs is a crucial step in building reliable AI applications that provide useful and accurate results.	Read	Watch
Train Your Model - Fine-Tuning LLM	Learn how to fine-tune an open-source LLM into a specialized expert for your specific task. Master the complete engineering workflow from data prep and QLoRA training to evaluation and deployment on the Hugging Face Hub.	Read	Watch

RAG and Context Engineering

Connect LLMs to external and unstructured data sources, so they can answer with up-to-date and private knowledge.

Lesson	Description	Tutorial	Video
Build a Chatbot with Memory	Learn how to build a chatbot that acts as a wellness coach using LangChain and Streamlit	Read	Watch
Use External Knowledge - Build a Cache-Augmented Generation (CAG) System	Learn to build a local Cache-Augmented Generation (CAG) system using LangChain and Ollama. Process documents and leverage full LLM context for knowledge tasks without retrieval.	Read	Watch
Create Knowledge for Your Models - Document Processing	Learn how to convert documents into knowledge for your AI applications. Process PDF files, including their images and tables, into structured data.	Read	Watch
Break It Down Right - Effective Chunking Strategies	Master the most critical step in RAG - chunking. Learn to move beyond simple splitting with structure-aware, semantic, and LLM-driven chunking techniques to build a knowledge base that powers context-aware AI.	Read	Watch
Search by Meaning - Embeddings and Vector Databases	Transform your text chunks into a searchable knowledge base. Learn to create semantic embeddings, perform similarity searches, and store your vectors in a production-ready database like Supabase with pgvector.	Read	Watch
Beyond Vector Search - Retrieving the Right Context	Upgrade your prototype RAG into a dependable, production-grade retriever. Combine BM25 and vector search, add a fast re-ranker, and use query reformulation (HyDE) to deliver precise, citable context to your LLM, keeping answers accurate and trustworthy.	Read	Watch
Build a Retrieval-Augmented Generation System	Learn to build an advanced Retrieval-Augmented Generation (RAG) system using LangChain, Ollama, and hybrid search. Process documents, create embeddings, and query your knowledge base with a local LLM.	Read	Watch

Agents and Workflows

Build the future of automation. Design intelligent agents that can reason, plan, and execute complex tasks on their own.

Lesson	Description	Tutorial	Video
Getting Started with LangGraph - Workflows and AI Agents	Master LangGraph by building an intelligent support ticket system. Learn the critical difference between predictable, developer-controlled workflows and flexible, LLM-driven agents. This tutorial provides the foundational skills for orchestrating complex, stateful AI applications.	Read	Watch
Teamwork Makes the Dream Work - Build Agentic Workflow	Build an agentic workflow that analyzes Reddit posts and generates a report based on the analysis. All using only local models.	Read	Watch
Thinking and Acting - Build an AI Agent	Build an AI agent that lets you to talk to your database. Working with a local LLM using LangChain and Ollama.	Read	Watch
Chat With Your Data - A Local MCP AI Agent	Build a secure, local-first AI agent that can chat with your files. This tutorial uses the Model Context Protocol (MCP), LangGraph, and Streamlit to create a powerful personal knowledge manager.	Read	Watch
Agentic RAG - Building an AI Financial Analyst Team	Build a multi-agent system with LangGraph that dynamically plans and retrieves financial data from stock APIs and SEC filings to answer complex questions, moving beyond simple RAG pipelines.	Read