#3636th of 41 in Learning and cookbooks

machine-learning-zoomcamp

Free ML engineering course from regression to Kubernetes deployment

Stars
14.8k
License
no license
Last commit
Oct 2026
Last release
Aug 2025
Language
Jupyter Notebook
Stars, last 29 weeks+2,152
31 Mar 2026: 12,657 stars7 Apr 2026: 12,730 stars14 Apr 2026: 12,797 stars21 Apr 2026: 12,841 stars28 Apr 2026: 12,884 stars5 May 2026: 12,925 stars12 May 2026: 12,963 stars19 May 2026: 12,999 stars26 May 2026: 13,034 stars2 Jun 2026: 13,087 stars9 Jun 2026: 13,138 stars16 Jun 2026: 13,166 stars23 Jun 2026: 13,204 stars30 Jun 2026: 13,362 stars7 Jul 2026: 13,516 stars14 Jul 2026: 13,598 stars21 Jul 2026: 13,670 stars28 Jul 2026: 13,734 stars4 Aug 2026: 13,781 stars11 Aug 2026: 13,826 stars18 Aug 2026: 13,881 stars25 Aug 2026: 13,974 stars1 Sept 2026: 14,050 stars8 Sept 2026: 14,185 stars15 Sept 2026: 14,393 stars22 Sept 2026: 14,515 stars29 Sept 2026: 14,620 stars6 Oct 2026: 14,688 stars13 Oct 2026: 14,741 stars20 Oct 2026: 14,809 stars

12,657 on 31 Mar 202614,809 on 20 Oct 2026

From GitHub's daily star counts.

Checked on GitHub.

Overview

Course materials for DataTalks.Club's Machine Learning Zoomcamp: pre-recorded lectures, notebooks and homework. It covers regression, classification, evaluation, tree models and deep learning, then deployment with FastAPI, Docker, AWS Lambda and Kubernetes. It can be followed self-paced or in a yearly live cohort with graded homework, peer review and certificates.

Who it is for: Programmers moving into ML engineering who want a project-based course

Strengths

  • Covers the full path from data prep to a deployed service, including Docker, Lambda and Kubernetes
  • Self-paced use is free, with lectures on YouTube and homework available unscored
  • Midterm and capstone projects produce end-to-end portfolio work
  • No local GPU needed; deep learning modules use cloud resources

Weaknesses

  • Certificates, scored homework and peer review only in the live cohort (starts September 14, 2026)
  • No license detected for the repository
  • Expects about one year of programming experience and command line comfort
  • Not aimed at advanced ML research; syllabus has no LLM module

What it needs

  • GPU optional
  • Needs Python, Docker, Kubernetes, AWS Lambda
  • Models: scikit-learn, XGBoost, TensorFlow, PyTorch, Keras

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