TL;DR: ML Zoomcamp is a free 4-month course teaching machine learning engineering. You’ll learn Python ML basics through to production deployment, build real projects, and join a supportive community. Next cohort starts September 2025. Join the course here.
Want to become a machine learning engineer?
Machine Learning Zoomcamp is a comprehensive, free four-month course that takes you from ML basics to production deployment.
What You’ll Learn in This Guide
- What Makes ML Zoomcamp Different
- Course Curriculum and Structure
- Learning Experience and Support
- Community and Resources
- Common Questions Answered
- Getting Started
What Makes ML Zoomcamp Different
- Free and Comprehensive: Complete ML engineering curriculum at zero cost
- Practical Focus: Real-world projects and hands-on exercises
- Community-Driven: Active Slack community for peer support
- Industry-Relevant: Tools and skills valued by employers
- Flexible Learning: Self-paced or structured cohort options
Explore more free courses at DataTalks.Club.
Course Curriculum
The course is divided into two main parts, carefully designed to build both your theoretical knowledge and practical skills.
Part 1: Machine Learning Foundations
Part 1 focuses on core machine learning concepts and their practical implementation using Python.
You’ll master:
- Linear Regression and Feature Engineering: Master feature creation, categorical variable handling, and regularization techniques
- Classification with Logistic Regression: Learn feature importance and model evaluation
- Decision Trees and Ensemble Methods: Explore gradient boosting and XGBoost implementation
- Neural Networks and Deep Learning: Build CNNs and implement transfer learning Tools you’ll learn:
- Jupyter Notebooks for efficient coding
- NumPy and Pandas for data manipulation
- Matplotlib and Seaborn for visualization
- Scikit-Learn for ML algorithms
- TensorFlow and Keras for deep learning
Part 2: Production Deployment
Part 2 focuses on model deployment, which involves putting machine learning models into production.
You’ll learn to deploy models using:
- Flask, Pipenv, and Docker: machine learning models deployment, enabling you to move your models from notebooks to services and applications.
- AWS Lambda and TensorFlow Lite: serverless deep learning, understanding how to efficiently operate within this paradigm.
- Kubernetes and TensorFlow Serving: automating deployment, scaling, and management of containerized applications.
- KServe (optional): an additional topic for those seeking advanced knowledge, offering insights into further enhancing deployment capabilities.
View the complete course syllabus on GitHub for a detailed week-by-week breakdown.
Theory and Practice
Our lectures aim to make machine learning theory accessible and engaging through real-world examples. Code demonstrations are provided directly in the lectures to show the implementation of concepts, enabling easier application in your projects.
Each concept comes with:
- Clear explanations
- Live code demos
- Hands-on exercises
- Real-world examples
View the complete course syllabus on GitHub for a detailed week-by-week breakdown.
Course Assignments and Scoring
Weekly Homework and Feedback
What to expect:
- Regular assignments
- Anonymous leaderboard
- Quick support via Slack
- Live Q&A sessions
Learning in Public
A unique feature is our “learning in public” approach, inspired by Shawn @swyx Wang’s article. We believe that everyone has something valuable to contribute, regardless of their expertise level.
Throughout the course, we actively encourage and incentivize learning in public. By sharing your progress, insights, and projects online, you earn additional points for your homework and projects.
Building Your Portfolio with Course Projects
If you’ve ever participated in an interview or conducted online research, you likely understand the significance of personal projects for a machine learning engineer role. Especially in case you don’t have any previous experience in machine learning and it’s your first job.
To receive a certificate, you’ll need to finalize and submit two projects: one during the midterm (Midterm project) and another at the end (Capstone project 1 and/or Capstone project 2). These projects allow you to choose a problem that interests you, find a suitable dataset, and develop your model. For the capstone project, you are also required to deploy your model into a web service, with an option for local deployment or on the cloud, earning bonus points.
For proactive participants, there’s an exciting opportunity to engage in an optional project and write an article. The article will require you to conduct research on a topic not covered in the course, encouraging you to explore beyond the curriculum’s confines.
You can find all the projects from the year 2022 in the final leaderboard. These projects allow you to apply everything you’ve learned and make a great addition to your GitHub profile.
DataTalks.Club Community
DataTalks.Club has a supportive community of like-minded individuals in our Slack. It is the perfect place to enhance your skills, deepen your knowledge, and connect with peers who share your passion. These connections can lead to lasting friendships, potential collaborations in future projects, and exciting career prospects.
Quick Start Guide
Machine Learning Zoomcamp offers a practical path to becoming an ML engineer. In just 4 months, you’ll gain:
- Hands-on experience with industry-standard tools (Python, TensorFlow, Docker)
- A portfolio of real-world ML projects
- Professional deployment skills
- Access to an active learning community
The next cohort starts on September 2025! Take the first step toward your ML engineering career. Register for the course and start your learning journey today!
Frequently Asked Questions
How is the course structured?
The course runs for 4 months and includes pre-recorded videos, live office hours, hands-on projects, and a vibrant community. You'll need around 10 hours per week for coursework and projects.How can I start learning?
You can choose between two learning paths: self-paced learning, where you can start immediately with pre-recorded materials freely available on GitHub and learn at your own pace, or joining our live cohort starting September 2025 to learn alongside peers, participate in live sessions, and earn a certificate.What's included in the live cohort?
The live cohort includes regular office hours with instructors, a structured learning path with deadlines, peer interaction and community support, the opportunity to earn a certificate, and access to all recorded sessions and office hours. Note that even if you're learning at your own pace, you still have access to all course materials and recordings.How do I get certified?
To earn a certificate, you’ll need to finalize and submit two projects: one during the midterm (Midterm project) and another at the end (Capstone project 1 and/or Capstone project 2). You'll also need to review 3 peers' projects by the deadline. Keep in mind that projects must be completed individually, and you must be part of a cohort to be eligible for certification.Can I join after the course has started?
Yes! While you might miss some homework deadlines, you can still join and get certified by completing the required projects. All course materials remain accessible.What Python knowledge do I need?
You should be familiar with basic Python concepts like variables, libraries, and Jupyter notebooks. If you need to brush up, we recommend taking our Introduction to Python course first.What technical setup do I need?
For machine learning modules, you only need a laptop with internet connection. For deep learning sections, we'll use cloud resources (like Saturn Cloud) for more intensive computations.What's the balance between theory and practice?
The course is heavily focused on practical implementation. We cover theoretical concepts at an intuitive level, emphasizing hands-on coding and real-world applications over mathematical derivations.How can I engage with the community?
Join our active Slack community, participate in office hours, and share your learning journey on social media with #mlzoomcamp. You can earn extra points for sharing your learning experience publicly.Is this course suitable for beginners?
Yes! If you have basic Python knowledge, you can start the course. The course is designed to be beginner-friendly, with step-by-step explanations of concepts, a practical hands-on learning approach, active community support in Slack, regular office hours for questions, and comprehensive learning materials.What are the prerequisites for the course?
The only requirement for this course is prior programming experience (1+ year) and familiarity with the command line.What's the difference between self-paced and cohort learning?
While all course materials are freely available for self-paced learning, joining a cohort offers additional benefits. You'll get a structured timeline with regular deadlines, active peer learning and discussion, live office hours and troubleshooting support, the opportunity to earn a certificate, and a shared learning experience with others facing similar challenges. The content is the same, but many students find the cohort structure helps them stay motivated and complete the course successfully.How can I make the most of this course for my career?
To maximize the course's career impact, we recommend starting your capstone project planning early and building a portfolio-worthy project that solves a real problem. Stay engaged with the Slack community and share your learning journey on social media using #mlzoomcamp. Take time to review and learn from other students' projects. When job hunting, use your project to demonstrate practical skills in applications and interviews - many of our alumni have successfully leveraged their course projects to demonstrate their machine learning capabilities during the hiring process.Quick Links
Ready to begin your ML engineering journey? Here’s everything you need: