Revise contribution guidelines and enhance documentation structure; add detailed contribution process, content organization, and community support information to improve clarity for new contributors.

kavaivaleri · kavaivaleri · commit b8cfaf80e705 · 2025-09-29T13:23:41.000+02:00
diff --git a/docs/index.md b/docs/index.md
@@ -1,24 +1,122 @@
 ---
-title: Documentation
+title: How To Contribute
 layout: default
 nav_order: 2
 description: "Course notes and resources for DataTalks.Club Zoomcamps"
 has_children: true
 ---
 
-# Course Documentation
+# How To Contribute
 {: .fs-9 }
 
-Welcome to the documentation section for DataTalks.Club Zoomcamps.
-{: .fs-6 .fw-300 }
+Welcome to the DataTalks.Club Zoomcamps Notes repository! This is a community-driven resource that helps thousands of learners worldwide. Your contributions make a real difference in supporting fellow data enthusiasts on their learning journey.
 
-1. **Choose a Course**: Pick one that matches your interests and career goals
-2. **Join the Community**: Connect with fellow learners on [Slack](https://datatalks.club/slack.html)
-3. **Start Learning**: Dive into the notes and begin your journey
-4. **Get Support**: Ask questions and share your progress
+## Ways to Contribute
 
-**Remember**: All courses are completely free and designed by practitioners for real-world application!
+- Contribute course notes and summaries
+- Report typos, grammatical errors, or unclear explanations
+- Fix broken links and outdated information
+- Correct technical inaccuracies
+- Improve existing explanations with better examples
+- Add visual aids like diagrams or screenshots
+- Reorganize content for better flow and readability
+
+## Detailed Contribution Process
+
+### 1. Getting Started
+
+#### Prerequisites
+- GitHub account
+- Basic knowledge of Git and Markdown
+- Familiarity with the course content you want to contribute to
+
+#### **Repository Setup**
+1. **Fork the Repository**: Click the "Fork" button on the main repository page
+2. **Clone Your Fork**: 
+   ```bash
+   git clone https://github.com/YOUR_USERNAME/zoomcamps-notes-faq.git
+   cd zoomcamps-notes-faq
+   ```
+3. **Add Upstream Remote**:
+   ```bash
+   git remote add upstream https://github.com/DataTalksClub/zoomcamps-notes-faq.git
+   ```
+
+### 2. Making Changes
+
+#### **Branch Creation**
+Always create a new branch for your changes:
+```bash
+git checkout -b feature/your-contribution-description
+```
+
+Use descriptive branch names like:
+- `fix/ml-zoomcamp-week3-typos`
+- `add/data-engineering-kafka-notes`
+- `improve/llm-zoomcamp-rag-examples`
+
+#### **Content Guidelines**
+
+**Writing Style**:
+- Use clear, concise language
+- Write in an educational, friendly tone
+- Include practical examples and real-world applications
+- Structure content with proper headings and bullet points
+
+**Technical Content**:
+- Test all code examples before submitting
+- Include necessary imports and dependencies
+- Add comments explaining complex logic
+- Provide context for when and why to use specific approaches
+
+**Formatting Standards**:
+- Use consistent Markdown formatting
+- Follow the existing file structure and naming conventions
+- Include proper front matter for new pages
+- Optimize images and use appropriate alt text
+
+### Submitting Your Contribution
+
+#### **Commit Best Practices**
+- Write clear, descriptive commit messages
+- Make atomic commits (one logical change per commit)
+- Use conventional commit format when possible:
+  ```
+  feat: add machine learning zoomcamp week 5 notes
+  fix: correct linear regression formula in week 2
+  docs: improve contribution guidelines
+  ```
+
+#### **Pull Request Process**
+1. **Push Your Branch**:
+   ```bash
+   git push origin your-branch-name
+   ```
+
+2. **Create Pull Request**:
+   - Go to your fork on GitHub
+   - Click "New Pull Request"
+   - Select your branch and the main repository's main branch
+   - Fill out the pull request template
+
+3. **Pull Request Description Should Include**:
+   - Clear title summarizing the changes
+   - Detailed description of what was added/changed
+   - Context for why the change is beneficial
+   - Any relevant issue numbers
+   - Screenshots for visual changes
+
+## Content Organization
+
+### Repository Structure
+```
+docs/
+├── machine-learning-zoomcamp/
+│   ├── 01-intro/
+│   ├── 02-regression/
+│   └── ...
+```
 
 ---
 
-*Need specific help? Join our [Slack community](https://datatalks.club/slack.html) and ask in the course-specific channels!*
+*Questions? Join our [Slack community](https://datatalks.club/slack.html) and ask in the `#general` channel or the course-specific channels. Our community is always happy to help new contributors get started!*
diff --git a/docs/machine-learning-zoomcamp/index.md b/docs/machine-learning-zoomcamp/index.md
@@ -16,91 +16,7 @@ A free 4-month course on ML Engineering: learn to build and deploy practical mac
 [Join the 2025 Cohort](https://airtable.com/shryxwLd0COOEaqXo){: .btn .btn-primary .fs-5 .mb-4 .mb-md-0 .mr-2 }
 [Watch on YouTube](https://www.youtube.com/playlist?list=PL3MmuxUbc_hIhxl5Ji8t4O6lPAOpHaCLR){: .btn .fs-5 .mb-4 .mb-md-0 }
 
+---
 
-## About ML Zoomcamp
-
-This is a practical course where you'll learn to build and deploy machine learning systems. We focus on the engineering side from training models to getting them to work in production.
-
-### Prerequisites
-- Prior programming experience (at least 1+ year)
-- Comfort with command line basics
-- No prior ML experience required
-
-### What You'll Learn
-- Core ML algorithms and when to use them
-- Data preparation and feature engineering
-- Model evaluation and selection
-- Deployment with Flask, Docker, and cloud platforms
-- Using Kubernetes for ML model serving
-- MLOps practices
-
-## Course Modules
-
-### Module 1: Introduction to Machine Learning
-Learn the fundamentals of ML and the CRISP-DM framework.
-- ML vs rule-based systems
-- Supervised learning basics
-- CRISP-DM methodology
-- Model selection concepts
-
-### Module 2: Machine Learning for Regression
-Build a car price prediction model.
-- Linear regression
-- Exploratory data analysis
-- Feature engineering
-- Regularization techniques
-
-### Module 3: Machine Learning for Classification
-Create a customer churn prediction system.
-- Logistic regression
-- Feature importance and selection
-- Categorical variable encoding
-
-### Module 4: Evaluation Metrics for Classification
-Learn proper model evaluation techniques.
-- Accuracy, precision, recall, F1-score
-- ROC curves and AUC
-- Cross-validation
-- Class imbalance handling
-
-### Module 5: Deploying Machine Learning Models
-Turn models into web services.
-- Model serialization
-- Flask web services
-- Docker containerization
-- Cloud deployment
-
-### Module 6: Decision Trees & Ensemble Learning
-Improve predictions with tree-based methods.
-- Decision trees
-- Random Forest
-- Gradient boosting (XGBoost)
-- Hyperparameter tuning
-
-### Midterm Project
-Apply your skills in a complete ML project.
-
-### Module 7: Neural Networks & Deep Learning
-Introduction to deep learning.
-- Neural network fundamentals
-- TensorFlow & Keras
-- Convolutional Neural Networks
-- Transfer learning
-
-### Module 8: Serverless Deep Learning
-Deploy models using serverless technologies.
-- AWS Lambda for ML
-- TensorFlow Lite
-- API Gateway
-
-### Module 9: Kubernetes & TensorFlow Serving
-Scale ML models with Kubernetes.
-- TensorFlow Serving
-- Model deployment and scaling
-- Load balancing
-
-### Module 10: KServe (Optional)
-Advanced model serving for production.
+This website is currently under development and contains community-contributed notes and resources. Content is being actively added and improved by the DataTalks.Club community. If you find any errors or would like to contribute, please see our contribution guidelines.
 
-### Capstone Project
-Build and deploy an end-to-end ML system.
diff --git a/index.md b/index.md
@@ -27,18 +27,3 @@ A comprehensive collection of notes, FAQ, and resources for the DataTalks.Club Z
 | [LLM Zoomcamp](https://github.com/DataTalksClub/llm-zoomcamp) | 10 weeks | June 2026 | Large Language Models course covering fine-tuning, RAG systems, and LLM application development | [Register](https://airtable.com/appPPxkgYLH06Mvbw/shr7WtxHEPXxaui0Q) | [![GitHub](https://img.shields.io/github/stars/DataTalksClub/llm-zoomcamp?style=social)](https://github.com/DataTalksClub/llm-zoomcamp) |
 | [Stock Markets Analytics Zoomcamp](https://github.com/DataTalksClub/stock-markets-analytics-zoomcamp) | 8 weeks | April 2026 | Financial data analysis and algorithmic trading strategies using Python and modern analytics tools | [Register](https://pythoninvest.com/course) | [![GitHub](https://img.shields.io/github/stars/DataTalksClub/stock-markets-analytics-zoomcamp?style=social)](https://github.com/DataTalksClub/stock-markets-analytics-zoomcamp) |
 | [Open Source LLM Zoomcamp](https://github.com/DataTalksClub/open-source-llm-zoomcamp) | – | Coming Soon | Hands-on course focused on open-source LLM tools, local deployment, and cost-effective AI solutions | – | [![GitHub](https://img.shields.io/github/stars/DataTalksClub/open-source-llm-zoomcamp?style=social)](https://github.com/DataTalksClub/open-source-llm-zoomcamp) |
-
-## Contributing
-
-We welcome contributions from the community! You can help by:
-
-- **Reporting Issues**: Found an error or broken link? Let us know
-- **Improving Content**: Submit pull requests with corrections or enhancements
-- **Adding Notes**: Share your own course notes and insights
-- **Answering Questions**: Help fellow learners in discussions
-
-### How to Contribute
-1. Fork this repository
-2. Create a new branch for your changes
-3. Make your improvements
-4. Submit a pull request with a clear description
