Getting Started

For the cross-course onboarding (registration, account setup, calendar, newsletter), read Joining a Cohort first.

This page covers the Machine Learning Zoomcamp specifics.

Star the GitHub repository

github.com/DataTalksClub/machine-learning-zoomcamp

Star it so you can find it later. All course materials are here, with each module having its own folder. Cohort-specific homework and deadlines are under cohorts/2025/.

See Resources for more.

Join the ML Zoomcamp Slack channel

After joining the DataTalks.Club Slack workspace, find:

#course-ml-zoomcamp

This is your primary support and Q+A channel for the course.

t.me/mlzoomcamp for course announcements. Telegram is announcement-only and is the most reliable place to catch important updates because Slack is busy.

The lectures are pre-recorded and available in the ML Zoomcamp YouTube playlist. The course is largely self-paced; live sessions are limited to a kickoff and occasional updates.

Bookmark the FAQ

The Machine Learning Zoomcamp FAQ is a comprehensive resource with answers from previous cohorts. Check it before posting in Slack.

Set up your environment

The course uses Python with scikit-learn, FastAPI for deployment, and AWS for cloud examples. See Environment Setup for the choices you need to make.

Start Module 1

Begin with Module 1: Introduction. For what each module covers, see Curriculum.

Where to look next

Prerequisites - what you need to know before starting.
Curriculum - the modules.
Environment Setup - Python, AWS, dev tools.
Project - the project rubric (2 of 3 projects required).
Resources - all ML-specific links in one place.
What’s New - changes for the current cohort.
Learning in Public - hashtag for ML is #mlzoomcamp.