datatalksclub/machine-learning-zoomcamp

★ 13,074Jupyter NotebookAudience · developerComplexity · 3/5Setup · moderate

Mindmap

mindmap
  root((ML Zoomcamp))
    What It Is
      Free 4 month course
      Beginner friendly
    Topics
      Regression and classification
      Deep learning
      Model deployment
    Tools
      Python scikit-learn
      TensorFlow PyTorch
      Docker Kubernetes
    Participation
      Self-paced anytime
      Live cohort September

mindmap root((ML Zoomcamp)) What It Is Free 4 month course Beginner friendly Topics Regression and classification Deep learning Model deployment Tools Python scikit-learn TensorFlow PyTorch Docker Kubernetes Participation Self-paced anytime Live cohort September

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Things people build with this

USE CASE 1

Learn machine learning from scratch through a structured four-month curriculum with video lectures and hands-on projects.

USE CASE 2

Build and deploy a machine learning model as a REST API using FastAPI and Docker.

USE CASE 3

Join the live September cohort for graded homework, peer project reviews, and a completion certificate.

Tech stack

PythonJupyter Notebookscikit-learnTensorFlowPyTorchDockerFastAPIKubernetes

Getting it running

Difficulty · moderate Time to first run · 30min

Deep learning sections require cloud GPU resources rather than a personal laptop.

In plain English

Machine Learning Zoomcamp is a free four-month online course that teaches machine learning engineering from the ground up. It starts with core concepts and algorithms: regression, classification, decision trees, and deep learning, then moves through to putting models into production using Docker, FastAPI, Kubernetes, and AWS Lambda. All materials live in this GitHub repository and recorded lectures are available on YouTube. The course is designed for people who already know how to program (at least a year of experience is recommended) but have no prior machine learning background. Deep learning sections that require more computing power use cloud resources rather than a personal laptop. The main tools used throughout are Python, NumPy, Pandas, scikit-learn, TensorFlow, and PyTorch. There are two ways to participate. Self-paced learners can start at any time, work through the modules at their own speed, and build projects for a portfolio, but homework is not graded and no certificate is issued. Live cohort participants follow a fixed September-to-December schedule, submit homework for automatic scoring on a public leaderboard, complete two projects with peer review, and earn a certificate after meeting the requirements. The 2025 cohort starts September 15. Each module has its own folder in the repository. Cohort-specific homework and deadlines are in a separate directory. Community support runs on the DataTalks.Club Slack workspace in a dedicated course channel, with announcements also posted to a Telegram channel. Topics covered span the full engineering pipeline: exploratory data analysis, model building, feature engineering, evaluation metrics, model packaging, API development, container-based deployment, and scaling on cloud infrastructure.

Copy-paste prompts

Prompt 1

Walk me through the Machine Learning Zoomcamp deployment module: how do I package a scikit-learn model with FastAPI and serve it inside a Docker container?

Prompt 2

How does the Machine Learning Zoomcamp course recommend setting up a cloud GPU environment for the deep learning modules?

Prompt 3

Show me how to submit homework for the Machine Learning Zoomcamp live cohort and check my score on the public leaderboard.

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