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
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 |
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 |
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 |
| 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 |
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 |
| 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 |
Build the future of automation. Design intelligent agents that can reason, plan, and execute complex tasks on their own.
| Lesson | Description | Tutorial | Video |
|---|---|---|---|
| Choosing Your Approach - Workflows vs Agents | Learn the difference between AI agentic workflows and autonomous agents. Understand key architectural patterns, decision criteria, tool use, memory, and HITL for LLM automation with LangChain concepts. | Read | |
| 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 |