https://github.com/DataTalksClub/machine-learning-zoomcamp
Learn ML engineering for free in 4 months!
https://github.com/DataTalksClub/machine-learning-zoomcamp
Last synced: 19 days ago
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Learn ML engineering for free in 4 months!
- Host: GitHub
- URL: https://github.com/DataTalksClub/machine-learning-zoomcamp
- Owner: DataTalksClub
- Created: 2020-04-17T04:29:23.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2024-10-26T09:33:50.000Z (6 months ago)
- Last Synced: 2024-10-29T11:22:35.831Z (6 months ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 11.1 MB
- Stars: 9,517
- Watchers: 160
- Forks: 2,243
- Open Issues: 4
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Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
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README
Machine Learning Zoomcamp: A Free 4-Month Course on ML Engineering
Master the fundamentals of machine learning, from regression and classification to deployment and deep learning.
Join #course-ml-zoomcamp Channel on Slack •
Telegram Announcements •
Course Playlist •
FAQ •
Tweet about the Course
## How to Enroll
### 2025 Cohort
- **Start Date**: September 2025
- **Register Here**: [Sign up](https://airtable.com/shryxwLd0COOEaqXo)
- **Stay Updated**: Subscribe to our [Google Calendar](https://calendar.google.com/calendar/?cid=cGtjZ2tkbGc1OG9yb2lxa2Vwc2g4YXMzMmNAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ)
### Self-Paced Learning
All course materials are freely available for independent study. Follow these steps:
1. Watch the course videos and work through the code.
2. Join the [Slack community](https://DataTalks.Club/slack.html) (`#course-ml-zoomcamp`).
3. Ask questions in Slack or refer to the FAQ.
4. Complete the homework assignments (solutions provided but attempt first).
5. Work on at least one project for deeper learning.
## Syllabus Overview
The course consists of structured modules covering the full ML pipeline, from fundamentals to advanced techniques.
### Prerequisites
- Prior programming experience (at least 1+ year)
- Comfort with command line basics
- No prior ML knowledge required
### Modules
#### [Module 1: Introduction to Machine Learning](01-intro/)
- ML vs Rule-Based Systems
- Supervised Learning
- CRISP-DM Framework
- Model Selection Process
- Environment Setup
- Homework
#### [Module 2: Machine Learning for Regression](02-regression/)
- Car Price Prediction Project
- Exploratory Data Analysis
- Linear Regression Basics
- Feature Engineering & Regularization
- Homework
#### [Module 3: Machine Learning for Classification](03-classification/)
- Churn Prediction Project
- Feature Selection & Encoding
- Logistic Regression
- Model Interpretation
- Homework
#### [Module 4: Evaluation Metrics](04-evaluation/)
- Accuracy, Precision, Recall
- ROC Curves & AUC
- Cross-Validation
- Homework
#### [Module 5: Deploying ML Models](05-deployment/)
- Saving & Loading Models
- Flask API Deployment
- Docker & Virtual Environments
- Cloud Deployment (AWS)
- Homework
#### [Module 6: Decision Trees & Ensemble Learning](06-trees/)
- Decision Trees
- Random Forest & Gradient Boosting
- Model Selection & Hyperparameter Tuning
- Homework
#### [Module 7: Neural Networks & Deep Learning](08-deep-learning/)
- TensorFlow & Keras
- Convolutional Neural Networks
- Transfer Learning
- Model Optimization & Regularization
- Homework
#### [Module 8: Serverless Deep Learning](09-serverless/)
- Introduction to Serverless
- AWS Lambda & TensorFlow Lite
- API Gateway
- Homework
#### [Module 9: Kubernetes & TensorFlow Serving](10-kubernetes/)
- TensorFlow Model Serving
- Kubernetes Basics
- Deploying ML Models to Kubernetes
- Homework
#### [Capstone Projects](projects/)
- Midterm & Final Projects integrating all learned concepts
## Community & Support
### **Getting Help on Slack**
Join the [`#course-ml-zoomcamp`](https://app.slack.com/client/T01ATQK62F8/C0288NJ5XSA) channel on [DataTalks.Club Slack](https://DataTalks.Club/slack.html) for discussions, troubleshooting, and networking.
To keep discussions organized:
- Follow [our guidelines](asking-questions.md) when posting questions.
- Review the [community guidelines](https://datatalks.club/slack/guidelines.html).
> We encourage [Learning in Public](learning-in-public.md)
## Sponsors & Supporters
A special thanks to our course sponsors for making this initiative possible!
Interested in supporting our community? Reach out to [[email protected]](mailto:[email protected]).
## About DataTalks.Club
DataTalks.Club is a global online community of data enthusiasts. It's a place to discuss data, learn, share knowledge, ask and answer questions, and support each other.
Website •
Join Slack Community •
Newsletter •
Upcoming Events •
Google Calendar •
YouTube •
GitHub •
LinkedIn •
Twitter
All the activity at DataTalks.Club mainly happens on [Slack](https://datatalks.club/slack.html). We post updates there and discuss different aspects of data, career questions, and more.
At DataTalksClub, we organize online events, community activities, and free courses. You can learn more about what we do at [DataTalksClub Community Navigation](https://www.notion.so/DataTalksClub-Community-Navigation-bf070ad27ba44bf6bbc9222082f0e5a8?pvs=21).