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 | |