Machine Learning Zoomcamp Coursework
My completed coursework from the Machine Learning Zoomcamp: regression, classification, and neural networks, taken all the way through to deployed models on Kubernetes.
Overview
The Machine Learning Zoomcamp is a free, project-based course from DataTalksClub that walks through the full lifecycle of a machine learning project, from framing a problem and training a first model to shipping it as a running service. This repository is my completed coursework: the notebooks, scripts, and deployment work I built while working through the camp end to end.
It isn't a single polished application so much as a record of the progression: each module builds on the last, and the repo is organized into module-1 through module-6 directories that group the work as the course moved from fundamentals into deployment and serving.
What's Covered
| Module | Topic |
|---|---|
| 1 | Introduction to Machine Learning |
| 2 | Machine Learning for Regression |
| 3 | Machine Learning for Classification |
| 4 | Evaluation Metrics |
| 5 | Deploying ML Models |
| 6 | Decision Trees & Ensemble Learning |
| 7 | Neural Networks & Deep Learning |
| 8 | Serverless Deep Learning |
| 9 | Kubernetes & TensorFlow Serving |
The arc is deliberate: start with the framing and a linear regression model, move into classification and the metrics that tell you whether a model is actually good, then take a trained model and put it behind a real interface. The later modules push deployment further: containers, serverless functions, and finally Kubernetes with TensorFlow Serving.
Stack
Python · scikit-learn · TensorFlow · Flask · Docker · Kubernetes, the same tools the course uses, kept close to how each module teaches them.
Takeaway
The part that stuck with me wasn't any single model. It was seeing a trained notebook become a service. Going from regression to deep learning and then deploying that work with Flask, Docker, and Kubernetes is what turned the coursework into a working understanding of how an ML project actually ships.