Awesome AI Agents › Courses and Tutorials
AI Bootcamp is a self-paced course repository that collects notebooks and written tutorials for engineers who want practical experience building AI systems rather than a purely theoretical treatment. The curriculum is organised in tracks. The foundations track covers Python data structures, typing, NumPy and Pandas for machine learning work, the linear algebra, calculus and probability needed to reason about models, linear baselines built end to end from feature scaling to evaluation, and PyTorch fundamentals including tensors, autograd, data loading, optimisers and complete training loops. The MLOps and production track walks through the lifecycle of shipping a model: exploratory analysis of a real dataset, data validation with pandera, feature engineering with scikit-learn pipelines, versioning with DVC, reproducible training pipelines, experiment tracking with MLflow and model tuning. Later material moves to generative AI and agents, covering local model serving with Ollama, retrieval-augmented generation, LangChain and LangGraph, prompt engineering, DSPy, fine-tuning, and multi-agent systems built with CrewAI. Examples reference a range of models including hosted and open-weight options. Every notebook can be opened directly in Google Colab through a badge in the README, so the material can be run without local setup, and each lesson links to a longer written tutorial hosted on the authors learning site. Some lessons also have accompanying video walkthroughs on the associated YouTube channel, and there is a Discord community for questions. The repository serves as the index and code companion to that broader course rather than as a standalone library, so the value is in the worked examples and their ordering. It suits developers moving from general software engineering into AI engineering, and readers who want a single sequenced path from machine learning basics through to agent frameworks.
https://github.com/curiousily/AI-Bootcamp
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