Machine Learning Zoomcamp is a free, hands-on course that teaches machine learning engineering through building and deploying real projects.
The course addresses the gap between learning machine learning theory and shipping production systems. It guides learners through the complete workflow: framing problems, preparing data, training and evaluating models, exposing them through APIs, containerizing applications, and deploying to cloud infrastructure. The curriculum covers practical tools including Python, NumPy, pandas, scikit-learn, TensorFlow, PyTorch, FastAPI, Docker, Kubernetes, and AWS Lambda, with hands-on work on regression and classification models using tree-based and deep learning approaches.
This course suits developers and data scientists who want to move beyond notebook experiments into production-ready machine learning work. It is structured as a self-paced program with video lectures, course materials in the repository, and community support through Slack and Telegram channels. The course emphasizes the engineering side of machine learning rather than pure algorithm theory, making it particularly valuable for those building end-to-end systems.
The project maintains active community engagement through multiple communication channels and welcomes contributions to course materials. The repository contains structured course content organized by cohort, with supporting documentation and frequently asked questions available to guide learners through the program.
