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alexeygrigorev/mlbookcamp-code
The code from the Machine Learning Bookcamp book
Companion code for the Machine Learning Bookcamp by Alexey Grigorev, covering classical ML through deep learning and deployment on AWS. Aimed at beginners-to-intermediate practitioners who learn best by following along with a book rather than piecing things together from docs.
Each chapter has a single focused notebook with real datasets (car prices, churn, credit scoring, clothing images) rather than toy examples. The deployment chapter actually goes end-to-end: Flask, Docker, Pipenv, and Elastic Beanstalk — not just model training. The serverless and Kubernetes chapters include working Dockerfiles and YAML manifests, not just prose. There's a free companion course (ML Zoomcamp) with homework assignments, making this useful as a structured self-study path without buying the book.
The repo is essentially a book appendix — no standalone value if you don't have the book or aren't following the Zoomcamp. The notebooks haven't been updated to reflect current library versions; TensorFlow and scikit-learn have moved enough that some cells will need fixing. There's no requirements.txt or environment file at the repo root — only the deployment chapter has a Pipfile, so reproducing any other chapter's environment is trial and error. The chapter 4 metrics notebook lives inside the chapter 3 folder, which is confusing and suggests the organization was never really cleaned up.