DataTalksClub / machine-learning-zoomcamp

Machine Learning Zoomcamp: A Free Course on ML Engineering
 A free, hands-on course on building, evaluating, and deploying machine learning systems.
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## Quick Links
| Resource | Link |
| --- | --- |
| Register | Sign up for the free 2026 cohort |
| Start date | September 14, 2026 |
| Learn | Course platform · Materials · Videos |
| Current cohort | 2026 syllabus and deadlines |
| Community | Slack · Course channel · Telegram |
| Help | Course docs · Logistics · FAQ |
## About the Course
Machine Learning Zoomcamp is a free, practical course for people who want to learn machine learning engineering by building and deploying real projects.
The course follows the complete path from a machine learning problem to a production service:
> Frame the problem, prepare the data, train and evaluate the model, expose it through an API, package it, and deploy it.
You will work with Python, NumPy, pandas, scikit-learn, TensorFlow, PyTorch, FastAPI, Docker, Kubernetes, and AWS Lambda. Along the way, you will build regression and classification models, evaluate them with appropriate metrics, train tree-based and deep learning models, and deploy machine learning services.
This is not a theory-only course. The emphasis is on applying machine learning to practical problems and turning trained models into services that other applications can use.
## Who Should Join
This course is for you if you:
- Already have programming experience and want to move into machine learning engineering.
- Want a structured introduction to machine learning without assuming prior ML knowledge.
- Want to understand both model development and production deployment.
- Learn best by completing homework and building end-to-end projects.
- Want portfolio projects that demonstrate practical machine learning skills.
- Benefit from cohort deadlines, community support, peer review, and certificate eligibility.
The course is especially relevant for software engineers, data analysts, data engineers, technical students, and other practitioners who want to build and deploy machine learning systems.
This course is probably not the right fit if you have no programming experience, want a non-technical introduction to AI, or are looking primarily for advanced machine learning research topics.
## Prerequisites
You do not need prior machine learning experience.
You should have:
- At least one year of programming experience.
- Comfort using the command line.
- A laptop with an internet connection.
Helpful, but not required:
- Familiarity with Python.
- Basic Git and GitHub experience.
- Experience working with data in notebooks or scripts.
Not required:
- Previous experience training machine learning models.
- Cloud or Kubernetes experience.
- A powerful computer or local GPU. The deep learning modules use cloud resources for more intensive computation.
## How to Take the Course
You can