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Course platform with deadlines and submission forms for homework assignments and projects • Course Channel on Slack (#course-ml-zoomcamp) •Telegram Announcements •Course Playlist •FAQ •Tweet about the Course

Learn machine learning engineering end-to-end, from core models to deploying real applications.

Build regression and classification models in Python, work with key algorithms like linear/logistic regression, decision trees, and deep learning, and then take them to production using Docker, FastAPI, Kubernetes, and AWS Lambda.

• What This Course Is About
• Prerequisites
• How to Join
• Syllabus
• Community & Getting Help
• Certificates
• Sponsors
• About DataTalks.Club

Machine Learning Zoomcamp teaches you the complete machine learning engineering, covering the entire pipeline: from building models with Python to deploying them in production environments.

You’ll master the key ML algorithms like linear regression, logistic regression, decision trees, and deep learning with TensorFlow and PyTorch, then learn to containerize with Docker, build APIs with FastAPI, and scale with Kubernetes and AWS Lambda.

You'll need:

• Prior programming experience (at least 1+ year)
• Comfort with command line basics

You don't need any prior experience with machine learning. We'll start from the basics.

Technical setup: For machine learning modules, you only need a laptop with an internet connection. For deep learning sections, we'll use cloud resources for more intensive computations.

You can join ML Zoomcamp either byfollowing a live cohort orlearning at your own pace.

All materials are freely available in this repository. Each module has its own folder (e.g.,01-intro,03-classification), and cohort-specific homework and deadlines are in thecohorts directory. Lectures are pre-recorded and available in thisYouTube playlist.

flowchart TD    A["Want to learn Machine Learning Zoomcamp"] --> B{"Do you want<br/>deadlines & a certificate?"}    B -->|Yes| C["Join Live Cohort"]    B -->|No / Not sure| D["Self-Paced Learning"]    C --> C1["Fixed schedule (Sept–Dec)"]    C --> C2["Scored homework + leaderboard"]    C --> C3["2 projects + peer review"]    C --> C4["Eligible for certificate"]    D --> D1["Start anytime, go at your own pace"]    D --> D2["Unscored homework & optional projects"]    D --> D3["No certificate"]

Start anytime. You get full access to materials and community support on Slack.

Complete homework assignments: homework and solutions are available on thecourse platform. Build a project for your portfolio.

Under self-paced learning, homework isn't scored, your project isn't peer-reviewed, and you can't earn a certificate.

2025 Cohort: Starts September 15. Register here:Fill in this form

Runs once per year (September–December).

Includes:

• Updated homework
• Automatic homework scoring and a leaderboard
• Project peer review
• Eligibility for a certificate after meeting all requirements

Even if you join after the official start date, you can still follow along — but note that some homework forms may already be closed. All active deadlines are listed on thecourse platform.

To earn acertificate, you'll need enough time to complete twoprojects and the required peer reviews. Details are in the Projects and Certificate sections.

We summarized the key differences between the two joining options in this table:

Ready to start?Join the 2025 cohort orstart with Module 1

Choose a problem that interests you, find a suitable dataset, develop your model, and deploy it as a web service.

There will be 3 projects:

1. Midterm Project afterModule 6: Decision Trees & Ensemble Learning
2. Capstone project 1 at the end of the course, afterModule 10: Kubernetes & TensorFlow Serving
3. Capstone project 2 at the end of the course, afterModule 10: Kubernetes & TensorFlow Serving

These projects allow you to apply everything you've learned and make a great addition to your GitHub profile and portfolio.

A local deployment architecture using Kubernetes with Kind from one of the students' projects

Some of the course projects from past cohorts:

• Blood cell classifier for cancer prediction: an end-to-end tool that segments and classifies blood cells from microscope images to assist in detecting signs of acute lymphoblastic leukemia (ALL)
• Waste classifier: an Xception-based image classifier on ~15,000 waste images, reaching 93.3% test accuracy, and serving predictions via a Flask API packaged in Docker

Machine Learning Zoomcamp certificate awarded upon successful completion

To receive a certificate, you'll need to complete and submit two projects:

1. Complete two projects: Submit either a midterm project and a capstone project, OR two capstone projects
2. Submit on time: Meet the project submission deadlines to qualify for certification
3. Peer review: Evaluate and provide feedback on 3 fellow students' projects during the peer review process

Machine Learning Zoomcamp was exhaustive, with very comprehensive content that covered concepts in depth. You can learn everything from the simplest concepts to preparing and deploying an ML model for production. Additionally, the entire community behind this course is highly participative and collaborative. I would like to thank Alexey Grigorev for all the knowledge he shared with us and his team for providing the support we needed to solve each problem we faced.

• Alexander Daniel Rios (Source)

Machine Learning Zoomcamp has been an incredible journey, thanks to the expert guidance of Alexey Grigorev. Hugely grateful to Alexey, Timur, and the entire DataTalksClub team for this course, and to my cohort batchmates for the invaluable support that enriched my learning experience. I’m thankful for this programme, which provided challenging coursework that is taught in a very structured and lucid way. The timely assignments & hands-on projects instill the sense of timely delivery, besides equipping us with practical acumen to solve real-life problems.

• Siddhartha Gogoi (Source)

Balancing the intensive Machine Learning Zoomcamp with my other engagements was no easy task, but the experience deepened my expertise in machine learning engineering, reinforced my passion for ML deployment and cloud technologies, and strengthened my resilience in handling real-world ML challenges. Thank you, Alexey Grigorev, for this course!

• Patrick Edosoma (Source)

Highly recommend the ML Zoomcamp for anyone wanting a structured path to production-ready machine learning. A big thank you - Alexey Grigorev and to the team at DataTalksClub for providing such a well-structured and engaging course.

• Abdiaziz Mohamed (Source 1,Source 2)

A huge thank you to Alexey Grigoriev for creating such an amazing course—and making it free! It’s truly inspiring.

• Guilherme Pereira (Source)

Huge thanks to Alexey Grigorev and the DataTalksClub community for the incredible support and clarity throughout. The open-source spirit and collaborative notes made the learning experience even richer.

• Rajendra Rawale (Source)

Ready to start?Join the 2025 cohort orstart with Module 1

• Slack:#course-ml-zoomcamp channel
• FAQ:Common questions and answers
• Study Groups: Connect with other learners

• Check theFAQ first
• Follow ourquestion guidelines
• Be helpful and respectful
• Share your learning journey

We encourage sharing your progress! Write blog posts, create videos, post on social media with #mlzoomcamp. It helps you learn better and builds your professional network.

Bonus: You can earn extra points for sharing your learning experience publicly.

Learn more:Learning in Public

Interested in sponsoring? Contactalexey@datatalks.club.

DataTalks.Club is a global online community of data enthusiasts. It's a place to discuss data, learn, share knowledge, ask and answer questions, and support each other.

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