ML Zoomcamp: Free Machine Learning Engineering Course

Most machine learning courses teach you to build models in Jupyter notebooks and stop there. You learn regression, classification, and neural networks, yet when it’s time to deploy your model to production, you’re left figuring it out alone. The gap between training a model and running it in a real application can derail your ML career before it starts.

ML Zoomcamp solves this by teaching you complete machine learning engineering covering the entire pipelines: 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.

With this machine learning course, you’ll get hands-on experience with the entire ML workflow and two portfolio-ready projects showing you can build, deploy, and maintain production machine learning systems.

TL;DR: Machine Learning 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. The next cohort starts on September 15, 2025. Join the course here.

Table of Contents

• What is Machine Learning Zoomcamp?
• Why Learn Machine Learning?
• Who Can Learn Machine Learning?
• Course Prerequisites
• Course Curriculum: What You’ll Learn
• How the Machine Learning Zoomcamp Works
• DataTalks.Club Community
• Machine Learning Zoomcamp vs. Bootcamp
• How to Get Started
• Frequently Asked Questions

What is Machine Learning Zoomcamp?

Machine Learning Zoomcamp is a free machine learning course that teaches you to build and deploy machine learning models in production environments—bridging the gap between training models in notebooks and running them in real-world applications.

While ML Zoomcamp covers the fundamental machine learning algorithms you need to know, it focuses heavily on the practical skills that most courses skip. Instead of stopping at model training, you’ll learn the complete production workflow:

• Model Persistence and Containerization: Learn how to save trained models properly and package them using Docker containers, making your applications portable and reproducible. You’ll understand why containerization is essential for production deployments and how tools like Docker and Kubernetes solve real deployment challenges.

• API Development and Service Integration: Discover how to transform your models into web services using FastAPI, enabling other applications to make predictions via HTTP requests. You’ll learn to integrate your models into existing systems and build complete end-to-end applications with tools like FastAPI, Docker, Kubernetes, and AWS Lambda.

• Deployment Strategies: Understand the fundamental differences between traditional server-based deployments (using virtual machines or Kubernetes clusters) and serverless architectures (like AWS Lambda). You’ll learn the trade-offs, cost implications, and when to choose each approach for your specific use case.

Why Learn Machine Learning?

Machine learning has become a foundational technology across industries, driving the rapid expansion of the global AI market valued at $184 billion in 2024 and projected to reach $826 billion by 2030.

As a subset of artificial intelligence, machine learning is essential for modern AI applications. It enables systems to learn from data and make predictions without hard-coded rules. This technology powers many applications you interact with daily: language models like ChatGPT that process and generate text, image generation tools like DALL-E, computer vision systems in autonomous vehicles, and recommendation engines used by streaming and e-commerce platforms.

This widespread adoption has created significant demand for skilled professionals. According to the World Economic Forum’s Future of Jobs Report 2025, AI and machine learning specialists are among the top three fastest-growing roles between 2025 and 2030, with an expected global net growth of 82%.

Who Can Learn Machine Learning?

Machine learning is a technical field that requires understanding of programming and mathematical concepts. While you don’t need a PhD in mathematics or computer science to start learning, you should be prepared to work with concepts from linear algebra, calculus, and statistics as you progress. You can start with basic understanding, but you’ll eventually need to grasp these concepts to debug models and make informed decisions.

The learning curve can be steep. Understanding why your model isn’t working, dealing with messy real-world data, and troubleshooting deployment issues takes time and patience. You’ll need to be willing to struggle through problems and dedicate time to consistent practice.

Programming Skills

Essential. Daily coding to implement algorithms, process data, and build models. Python is most common.

Mathematical Foundation

Important. Linear algebra, calculus, and statistics. Start with basics, deepen over time.

Problem-Solving Mindset

Critical. Debugging models and troubleshooting require patience and systematic thinking.

Time Commitment

Significant. Building competence takes several months of consistent practice.

If this sounds like you, you’re in the right place:

• You are comfortable with programming (1+ year)
• You are comfortable with mathematical thinking
• You are willing to struggle through problems
• You are willing to dedicate time to consistent practice (expect several months to build competence)

People from various backgrounds enter the field: software developers, data analysts, researchers, and career changers with technical experience. Success typically depends on having a foundation in programming, dedicating time to study and practice, and working through problems systematically.

Course Prerequisites

This course is designed for anyone with programming experience who wants to learn machine learning engineering. You don’t need a background in mathematics, statistics, or data science to get started.

The only requirement for this course is prior programming experience (1+ year) and familiarity with the command line. If you’re completely new to programming, consider spending a few weeks learning the basics before starting the course.

Software Developers

Adding ML skills

Data Analysts

Moving to ML

Students

Practical skills

Career Changers

With coding background

The course is particularly suited for:

• Software developers and engineers who want to add machine learning to their skillset
• Data analysts looking to move into machine learning roles
• Students in computer science or related fields seeking practical ML skills
• Career changers with programming experience interested in AI and data science
• Technical professionals who want to understand how to deploy ML models in production

While having experience with Python is helpful, the course covers the essential libraries you’ll need. The key requirement is being comfortable with programming concepts and willing to commit time to learning and completing projects.

Course Curriculum: What You’ll Learn in the Machine Learning Zoomcamp

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..

Topic	Description	Tools
Linear Regression and Feature Engineering	Master feature creation, categorical variable handling, and regularization techniques	NumPy, Pandas, Scikit-Learn
Classification with Logistic Regression	Learn feature importance and model evaluation	Scikit-Learn, Matplotlib
Decision Trees and Ensemble Methods	Explore gradient boosting and XGBoost implementation	XGBoost, Scikit-Learn
Neural Networks and Deep Learning	Build CNNs and implement transfer learning	TensorFlow, PyTorch, Keras

We’ll also use Jupyter Notebooks for development, Matplotlib and Seaborn for visualization

Part 2: Production Deployment

Part 2 focuses on model deployment, which involves putting machine learning models into production:

Topic	Description	Tools
Model Deployment	Move models from notebooks to services and applications	FastAPI, Pipenv, Docker
Serverless Deep Learning	Deploy models efficiently using serverless architecture	AWS Lambda, ONNX Runtime
Container Orchestration	Automate deployment, scaling, and management of containerized applications	Kubernetes, TensorFlow Serving
KServe (optional)	Advanced deployment capabilities for production ML systems	KServe

Capstone Project

During ML Zoomcamp, you’ll finalize and submit two capstone projects:

• Midterm project in the middle of the course

• Capstone project 1 and/or Capstone project 2 at the end of the course

These projects allow to apply everything you’ve learned and make a great addition to your GitHub profile. You will choose a problem that interests you, find a suitable dataset, develop your model, and deploy it as a web service.

Students from previous cohorts built a wide range of projects. For example, one graduate created a blood cell classifier for cancer prediction and another built a waste classifier with over 15,000 images, achieving 93% accuracy.

How the Machine Learning Zoomcamp Works

Theory and Practice

Our lectures make machine learning theory accessible and engaging through real-world examples. We provide code demonstrations directly in the lectures to illustrate the implementation of concepts, making it easier to apply them in your projects.

GitHub Repository: The Central Hub of the Course

All course materials live in the GitHub repository. It serves as a central hub for the course, providing easier navigation through all course materials.

The lectures are pre-recorded and available on YouTube in our official Machine Learning Zoomcamp YouTube Playlist, so you can watch at your own pace.

Homework Assignments

To strengthen your understanding of the course material, we provide homework assignments for each module.

While these assignments don’t contribute to your certification, they serve as valuable practice opportunities and your performance is displayed on an optional anonymous leaderboard.

Your performance gets recorded on an anonymous leaderboard, fostering healthy competition among participants and encouraging you to strive for excellence.

You can earn additional points through learning in public, the process of documenting and sharing your journey on blogs, YouTube, or social media platforms. Our upcoming section explores this concept in detail.

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.

This not only demonstrates your knowledge but also builds a portfolio of valuable content. Sharing your work online also helps you get noticed by social media algorithms, reaching a broader audience and creating opportunities to connect with individuals and organizations you may not have encountered otherwise.

Many of our graduates have shared that their social media presence has helped them attract job offers and collaborations.

How to Get a Certificate

To receive a certificate, you’ll need to finalize and submit a midterm and a capstone project:

1. Complete projects: Submit both a midterm project and a capstone project
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

What is DataTalks.Club Community? A Place to Connect and Learn with Other Machine Learning Professionals

DataTalks.Club is a global community of 80,000+ data professionals who connect on Slack to share knowledge, ask career questions, and discuss everything from analytics and visualization to machine learning and data engineering. As one of the largest digital groups dedicated to data, it’s where you’ll find data scientists, ML engineers, data analysts, and enthusiasts at all career stages.

When you join a cohort, the dedicated course channel becomes your home base. Here, you’ll troubleshoot problems with peers working through the same challenges and share your progress and insights.

Beyond peer support, there are two ways to get help: our FAQ repository covers common questions and technical issues and the @ZoomcampQABot in Slack provides quick answers when you need them.

Machine Learning Zoomcamp vs. Bootcamp: What’s the Difference?

We often get asked what the difference is between the Machine Learning Zoomcamp and paid bootcamps.

Below, we list the key features of the Machine Learning Zoomcamp and how they compare to paid bootcamps.

Feature	ML Zoomcamp (Cohort)	ML Zoomcamp (Self-paced)	Paid Bootcamps
Cost	Free	Free	$2,000–$10,000+
Format	4-month cohort with fixed schedule	Learn anytime at your own pace	Fixed schedule, instructor-led
Homework	Weekly scored assignments	Available but no scoring	Weekly with instructor feedback
Projects	Capstone project with peer review and scoring	Build on your own, no evaluation and scoring	Instructor-reviewed projects
Certificate	Yes, after completing project + peer reviews	No certificate	Certificate of completion
Community Support	Active Slack + optional live Q&A sessions	Slack community only	Instructor-led, 1:1 or group mentorship
Learning in Public	Encouraged with bonus points	Optional	Rarely emphasized
Timeline	4 months (Sep–Dec 2025)	Flexible, self-paced	Typically 12–24 weeks
Best For	Career switchers or experienced engineers wanting community and accountability	Self-motivated learners exploring machine learning	Those needing intensive structured mentorship and guidance

How to Get Started with the Machine Learning Zoomcamp

To get started with the Zoomcamp, you can either join a live cohort or learn at your own pace.

Learn at Your Own Pace

The self-paced mode is a great way to learn at your own pace. You’ll get access to the course materials and the community. You can start learning immediately and complete the course at your own pace.

All you need is to go to the Machine Learning Zoomcamp GitHub repository and start learning. It serves as a central hub for the course for easier navigation through the course materials. All the lectures are pre-recorded and available on YouTube in our official Machine Learning Zoomcamp YouTube Playlist, so you can watch at your own pace.

You can also join the DataTalks.Club Slack community to get help and support from the community on the #course-ml-zoomcamp channel.

Remember, self-paced learning does not include homework submissions, project evaluations, or the ability to earn a certificate. To receive certification, you need to join an active cohort.

Join a Live Cohort

When you join a live cohort, you’ll work through the same materials as self-paced learners, but with the added structure of a published schedule and the energy of hundreds of peers progressing alongside you.

Each module typically spans one week, you watch the lectures, complete hands-on exercises, and submit a homework assignment. Your submissions get scored and appear on an anonymous leaderboard.

After completing all modules, the project phase begins. You’ll build your own end-to-end machine learning project, submit it through our form, and peer-review at least three other students’ projects while yours gets reviewed by your peers. This reciprocal process gives you valuable feedback on your work and exposes you to different approaches and solutions you might not have considered.

Ready to join ML Zoomcamp? Here’s how it works:

1. Register for the course, you’ll be automatically accepted into the next cohort
2. Join the DataTalks.Club Slack community and the #course-ml-zoomcamp channel for updates, questions, and peer support
3. (Optional) Get a head start by exploring the GitHub repository and watching lectures before the cohort officially begins
4. When the cohort starts, you’ll receive an email with the full schedule and submission deadlines
5. Follow the weekly rhythm: watch lectures, complete exercises, submit homework
6. Complete and submit your midterm project and capstone project, then peer-review three other projects
7. Receive your certificate once your projects pass peer review

The entire journey takes 4 months from start to certificate, and you’ll be part of a global cohort tackling the same challenges at the same time.

Frequently Asked Questions