ML Zoomcamp 2025 Pre-Course Live Q&A | Free Machine Learning Course
Get a full overview of the Machine Learning Zoomcamp 2025, a free, hands-on course that teaches you how to take machine learning models from notebooks to production.
The instructor explains the course structure, updates, prerequisites, certification process, and what’s new in the 5th edition, starting September 15th.
What You'll Learn on This Course
Build and deploy ML models using Scikit-learn, PyTorch, Docker, AWS Lambda, and Kubernetes
Apply best practices for packaging, serving, and scaling models
Gain real-world skills through project-based assignments and peer reviews
Course Highlights
Format: Free live cohort with weekly homework and deadlines
Duration: ~5-10 hours per week
Modules Updated: 5, 8, and 9 (new PyTorch deep learning section; TensorFlow still optional)
Environment: Works seamlessly with GitHub Codespaces (Linux, Docker, Python pre-installed)
Prerequisites: Basic Python and command-line skills (Bash). Linear algebra helpful but not required.
Certification: Complete 2 out of 3 projects + peer review → earn a certificate for LinkedIn
Who It's For
ML Engineers and Data Scientists who want practical deployment experience
Learners aiming to strengthen their model deployment skills
Resources:
Course: https://github.com/DataTalksClub/mach...
Telegram: https://t.me/mlzoomcamp
Workshop on uv and FastAPI: • How to Deploy Machine Learning Models with...
Book: https://mlbookcamp.com/
Articles: https://datatalks.club/articles.html
Interview with Pastor: • From Medicine to Machine Learning: How Pub...
TIMECODES
0:34 Main course entry point: GitHub page overview; course starts September 15.
1:33 This is the fifth edition of ML Zoomcamp.
2:03 Updated modules: 5, 8, and 9 (4 of 10 total). Modules 1–4 and 6 remain unchanged.
3:06 Free bootcamp format; no job placement provided.
3:41 Target audience: ML engineers and practitioners focused on deployment.
4:23 Course scope: limited computer vision, one module on image classification basics.
5:13 Deep learning module now uses PyTorch; TensorFlow remains optional.
5:35 RAG (Retrieval-Augmented Generation) is not included; it’s covered in LLM Zoomcamp.
6:08 Prerequisites: comfortable with Python and command line (Bash).
9:35 Recommended setup: GitHub Codespaces (Linux + Docker + Python, free).
10:15 No prior experience with PyTorch or TensorFlow required.
13:02 Recommended companion resource: Machine Learning Bookcamp.
14:37 Math level: minimal; basic linear algebra is helpful but not required.
17:06 Hardware requirements: lightweight; can be completed using a tablet + Codespaces.
18:33 Recommended approach: use ChatGPT or LLMs to explain code line by line.
25:06 Starting with zero programming experience is difficult and requires strong motivation.
26:51 Learning outcome: sufficient skills for an entry-level ML Engineer role.
27:11 Certificate awarded upon completion (suitable for LinkedIn).
28:59 Live cohort format with homework deadlines.
32:39 Course materials are pre-recorded; only one live session (launch stream).
36:44 Certification rule: complete at least two of three projects (midterm + two finals).
37:34 Estimated workload: 5-10 hours per week.
39:56 Projects designed to serve as portfolio pieces for job applications.
51:05 Homework optional; projects mandatory for certification.
52:55 Peer review required; skipping it results in project and course failure.
55:04 All existing course videos available to watch immediately.
👋🏼 Support/inquiries
If you want to support our community, use this link - https://github.com/sponsors/alexeygri...
If you’re a company, reach us at [email protected]
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