Machine learning zoomcamp
Learn ML engineering for free in 4 months! Register here 👇🏼
Machine Learning Zoomcamp: A Free 4-Month Course on ML Engineering The project is written primarily in Jupyter Notebook, first published in 2020. It has gained significant community traction with 13,248 stars and 2,975 forks on GitHub. Key topics include: course, deployment, docker, fastapi, kubernetes.
Quick Links
| Resource | Link |
|---|---|
| Course materials | GitHub repository |
| Video lectures | YouTube playlist |
| Documentation | Zoomcamp Logistics · ML Zoomcamp |
| Course platform (deadlines, homework) | courses.datatalks.club |
| Slack channel | #course-ml-zoomcamp |
| Announcements | Telegram |
| FAQ | FAQ document |
About the Course
Machine Learning Zoomcamp teaches complete machine learning engineering, covering the entire pipeline: from building models with Python to deploying them in production environments.
<p align="center"> <img src="https://github.com/DataTalksClub/datatalksclub.github.io/blob/main/images/posts/2024-04-11-guide-to-free-online-courses-at-datatalks-club/ml_zoomcamp_overview_horizontal_2025.png" alt="ML Zoomcamp course overview showing progression from ML algorithms (Python, NumPy, Pandas, Scikit-learn) to deployment (Docker, FastAPI, Kubernetes)" title="ML Zoomcamp course overview: ML algorithms to deployment" width="500" /> </p>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.
Who Should Join
This course is for software engineers, data analysts, and anyone with programming experience who wants to become a machine learning engineer. You don't need any prior machine learning experience; the course starts from the basics.
Prerequisites
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 the machine learning modules, you only need a laptop with an internet connection. For the deep learning sections, we'll use cloud resources for more intensive computations.
How to Take the Course
There are two ways to follow the course: live and self-paced.
| Live Cohort | Self-Paced | |
|---|---|---|
| Start | September 2026 | Anytime |
| Lectures | Pre-recorded | Pre-recorded |
| Homework | Graded | Available but not scored |
| Leaderboard | ✅ Yes | ❌ No |
| Peer Review | ✅ Yes | ❌ No |
| Certificate | ✅ Yes | ❌ No |
| Cost | Free | Free |
| Register | Sign up here | Just start learning! |
[!IMPORTANT]
"Live cohort" does not mean live classes. All lectures are pre-recorded. "Live" means working alongside others with deadlines, scored homework, a leaderboard, peer review, and a certificate at the end. The live cohort runs once per year (September to December).
To earn a certificate, you'll complete two projects (midterm + capstone, or two capstones) and the required peer reviews during a live cohort.
Self-paced steps:
- Follow the materials on GitHub
- Ask questions and share progress in Slack
- Do the homework (self-checked) and build a project for your portfolio
Syllabus
Module 1: Introduction to Machine Learning
Learn the fundamentals: what ML is, when to use it, and how to approach ML problems using the CRISP-DM framework.
- ML vs rule-based systems
- Supervised learning basics
- CRISP-DM methodology
- Model selection concepts
- Environment setup
Module 2: Machine Learning for Regression
Build a car price prediction model while learning linear regression, feature engineering, and regularization.
- Linear regression (from scratch and with scikit-learn)
- Exploratory data analysis
- Feature engineering
- Regularization techniques
- Model validation
Module 3: Machine Learning for Classification
Create a customer churn prediction system using logistic regression and learn about feature selection.
- Logistic regression
- Feature importance and selection
- Categorical variable encoding
- Model interpretation
Module 4: Evaluation Metrics for Classification
Learn how to properly evaluate classification models and handle imbalanced datasets.
- Accuracy, precision, recall, F1-score
- ROC curves and AUC
- Cross-validation
- Confusion matrices
- Class imbalance handling
Module 5: Deploying Machine Learning Models
Turn your models into web services and deploy them with Docker and cloud platforms.
- Model serialization with Pickle
- FastAPI web services
- Docker containerization
- Cloud deployment
Module 6: Decision Trees & Ensemble Learning
Learn tree-based models and ensemble methods for better predictions.
- Decision trees
- Random Forest
- Gradient boosting (XGBoost)
- Hyperparameter tuning
- Feature importance
Midterm Project
Apply Modules 1-6 in an end-to-end project: pick a dataset, train a model, and deploy it as a web service.
Module 8: Neural Networks & Deep Learning
Introduction to neural networks using TensorFlow and Keras, including CNNs and transfer learning.
- Neural network fundamentals
- PyTorch
- TensorFlow & Keras
- Convolutional Neural Networks
- Transfer learning
- Model optimization
Module 9: Serverless Deep Learning
Deploy deep learning models using serverless technologies like AWS Lambda.
- Serverless concepts
- Deploying Scikit-Learn models with AWS Lambda
- Deploying TensorFlow and PyTorch models with AWS Lambda
- API Gateway
Module 10: Kubernetes & TensorFlow Serving
Learn to serve ML models at scale using Kubernetes and TensorFlow Serving.
- Kubernetes basics
- TensorFlow Serving
- Model deployment and scaling
- Load balancing
Capstone Project 1
A larger end-to-end project at the end of the course, after Module 10.
Capstone Project 2
An optional second capstone, which together with the midterm or first capstone counts toward your two required projects.
Projects
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:
- Midterm Project after Module 6: Decision Trees & Ensemble Learning
- Capstone project 1 at the end of the course, after Module 10: Kubernetes & TensorFlow Serving
- Capstone project 2 at the end of the course, after Module 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.
Project Examples from Past Cohorts
<p align="center"> <img src="https://github.com/DataTalksClub/datatalksclub.github.io/blob/main/images/posts/2025-08-11-tab-1-how-to-build-blood-cell-classifier-for-cancer-prediction-case-study-from-ml-zoomcamp/image9.png" alt="A local deployment architecture using Kubernetes with Kind from one of the students' projects" title="Student project: local Kubernetes deployment with Kind" width="500" align="center" /> <p align="center"> <i> A local deployment architecture using Kubernetes with Kind from one of the students' projects </i> </p> </p>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
Certificate
<p align="center"> <img src="https://github.com/DataTalksClub/datatalksclub.github.io/blob/main/images/posts/2023-08-17-machine-learning-zoomcamp/ml-zoomcamp-certificate.jpg" alt="Machine Learning Zoomcamp certificate of completion awarded after successfully completing projects and peer reviews" title="ML Zoomcamp Certificate of Completion" width="500" align="center" /> <p align="center"> <i> Machine Learning Zoomcamp certificate awarded upon successful completion </i> </p> </p>To receive a certificate, you'll need to complete and submit two projects:
- Complete two projects: submit either a midterm project and a capstone project, OR two capstone projects
- Submit on time: meet the project submission deadlines to qualify for certification
- Peer review: evaluate and provide feedback on 3 fellow students' projects during the peer review process
See the certificate guide for how the certificate is issued and how to add it to LinkedIn.
Testimonials
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.
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.
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!
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.
A huge thank you to Alexey Grigoriev for creating such an amazing course—and making it free! It's truly inspiring.
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.
Community & Support
Getting Help on Slack
Join the #course-ml-zoomcamp channel on DataTalks.Club Slack for discussions, troubleshooting, and networking.
To keep discussions organized:
- Check the FAQ first.
- Follow our question guidelines when posting questions.
- Review the community guidelines.
Learning in Public
We encourage sharing your progress! Write blog posts, create videos, and post on social media with #mlzoomcamp. It helps you learn better and builds your professional network. You can also earn extra points for sharing your learning experience publicly.
Learn more: Learning in Public.
Sponsors
Interested in sponsoring? Contact alexey@datatalks.club.
FAQ
A few common questions. For everything else, see the full Machine Learning Zoomcamp FAQ.
Q: Is this course really free?<br/>
A: Yes. All videos, materials, and homework are free and open-source.
Q: Do I need prior machine learning experience?<br/>
A: No. The course starts from the basics. You just need about a year of programming experience and comfort with the command line.
Q: What does "live cohort" mean? Are there live classes?<br/>
A: No mandatory live classes. All lectures are pre-recorded. "Live" means deadlines, scored homework, a leaderboard, peer review, and certificate eligibility.
Q: Can I take it self-paced, and will I get a certificate?<br/>
A: Yes, you can start anytime. Certificates require completing two projects and the peer reviews during a live cohort.
About DataTalks.Club
<p align="center"> <img width="40%" src="https://github.com/user-attachments/assets/1243a44a-84c8-458d-9439-aaf6f3a32d89" alt="DataTalks.Club"> </p> <p align="center"> <a href="https://datatalks.club/">DataTalks.Club</a> 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. </p> <p align="center"> <a href="https://datatalks.club/">Website</a> • <a href="https://datatalks.club/slack.html">Join Slack Community</a> • <a href="https://us19.campaign-archive.com/home/?u=0d7822ab98152f5afc118c176&id=97178021aa">Newsletter</a> • <a href="http://lu.ma/dtc-events">Upcoming Events</a> • <a href="https://www.youtube.com/@DataTalksClub/featured">YouTube</a> • <a href="https://github.com/DataTalksClub">GitHub</a> • <a href="https://www.linkedin.com/company/datatalks-club/">LinkedIn</a> • <a href="https://x.com/DataTalksClub">X</a> </p>All the activity at DataTalks.Club mainly happens on Slack. We post updates there and discuss different aspects of data, career questions, and more.
At DataTalks.Club, we organize online events, community activities, and free courses. You can learn more about what we do at DataTalks.Club docs.
Contributors
Showing top 12 contributors by commit count.
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