ML Zoomcamp Module 1

I just completed Module 1: Introduction to Machine Learning from ML Zoomcamp 2026 🎉

This module covered:
🔹 Difference between ML and rule-based systems
🔹 What supervised machine learning is
🔹 CRISP-DM: a framework for structuring ML projects
🔹 The model selection step
🔹 Setting up the environment (Python, Jupyter, etc.)
🔹 Quick refreshers on NumPy, linear algebra, and Pandas
✨ My key takeaway: Practice. Practice. Practice.

🤔 Something I found interesting: How linear algebra is challenging!
➡️ Next step → Module 2: How to gain hands-on, supervised learning

mlzoomcamp @alexey Grigorev

Comments

[Doyoon Kim]

Congrats on finishing Module 1! I found that bridging the introductory concepts with a small end‑to‑end pipeline—say, a FastAPI wrapper around a lightweight model and a Redis‑backed feature store—helps solidify the theory while exposing production trade‑offs early on. Have you tried integrating any of the exercises with a vector‑DB retrieval step for downstream inference?

[AnnalieseTech]

I haven't! Have you?