🧠 ML-CaPsule
📖 Code of Conduct • 🤝 Contributing Guidelines • 🗺️ Learning Roadmap • 📋 Project Template • 🔀 PR Template
Hands-on Machine Learning — From Basics to Advanced
🚀 About
ML-CaPsule is a comprehensive, open-source learning hub for aspiring and experienced data science enthusiasts. Whether you lack a mentor or want structured, project-based learning — this repository helps you master Machine Learning, Deep Learning, NLP, Computer Vision, and more through 500+ real-world projects.
💡 No mentor? No problem. Learn by building, become interview-ready, and grow with a thriving open-source community.
ML Fundamentals
Statistics & Analytics
Data Science
🛠️ Tech Stack
📊 Repository Stats
📑 Table of Contents
| 📈 | Why Machine Learning? | 🗂️ |
| 📚 | Pre-requisites | 📦 |
| 🤖 | ML Algorithm Recommendation Guide | ⭐ |
| 📂 | All Projects | 🔗 |
| ⚙️ | Contribution Guidelines | 📖 |
| ✨ | Contributors | ❤️ |
| 🔍 | Notebook Health Check | 📝 |
📈 Why Machine Learning?
Machine learning automates analytical model building — enabling systems to learn from data, identify patterns, and make decisions with minimal human intervention. It sits at the heart of modern AI.
Why It Matters
| Benefit | Impact |
|---|---|
| 🔍 Pattern Discovery | Uncover hidden trends in massive datasets |
| ⚡ Automation | Reduce manual analysis and repetitive tasks |
| 🎯 Personalization | Power recommendations at Netflix, Amazon & Spotify |
| 🏥 Healthcare | Early disease detection & medical imaging |
| 💼 Business Edge | Used by Google, Meta, Uber as a competitive differentiator |
📚 Pre-requisites
Python Path
| Step | Resource | Link |
|---|---|---|
| 1️⃣ | Install Python | python.org |
| 2️⃣ | Learn Python Basics | W3Schools Python ML |
| 3️⃣ | ML with Python Course | freeCodeCamp ML |
🎨 Getting Started with R & RStudio — Click to expand
R is an open-source language built for statistical computing, data analytics, and scientific research.
Installation
- Install R → Download from CRAN
- Install RStudio → Download RStudio Desktop
📺 Recommended Video Tutorials
| # | Course | Link |
|---|---|---|
| 1 | R Programming Full Course (freeCodeCamp) | Watch → |
| 2 | R Basics Tutorial (Edureka) | Watch → |
| 3 | Data Science with R (Simplilearn) | Watch → |
| 4 | Intro to R for Data Science (DataCamp) | Watch → |
🗂️ Topics Covered
1. 📥 Data Extraction
Methods for constructing variable combinations to describe data accurately.
- Web Scraping — Beautiful Soup
2. 📊 Visualization
Expose patterns, trends & correlations visually.
- Libraries — Seaborn, Pandas, Matplotlib
3. 🎯 Feature Selection
Select relevant features to boost model accuracy.
- Library — scikit-learn
- Learn more — Feature Selection Guide
4. 📐 Statistics
| Area | Topics |
|---|---|
| Analytics | Descriptive, Diagnostic, Predictive, Prescriptive |
| Probability | Conditional, Bayes' Theorem, Distributions |
| Central Tendency | Mean, Mode, Variance, Skewness, Kurtosis |
| Hypothesis Testing | Z-Test, T-Test, ANOVA, Chi-Square |
| Regression | Linear & Multiple Linear Regression |
5. 🔬 Data Science
Multidisciplinary field combining statistics, ML, computing, and domain expertise to extract insights and drive decisions across industries.
📦 Dataset Resources
🗄️ Click to explore all datasets — Classification, Regression, NLP, CV & more
🟢 Classification
| # | Dataset | Description | Level | Download |
|---|---|---|---|---|
| 1 | Titanic | Predict passenger survival | ⭐ | Kaggle |
| 2 | Iris Flower | Classify 3 iris species | ⭐ | UCI |
| 3 | Breast Cancer Wisconsin | Malignant vs benign tumors | ⭐ | UCI |
| 4 | Heart Disease | Predict from clinical features | ⭐⭐ | UCI |
| 5 | Pima Indians Diabetes | Diabetes onset prediction | ⭐ | Kaggle |
| 6 | Adult Income | Income >$50K prediction | ⭐⭐ | UCI |
| 7 | Bank Marketing | Term deposit subscription | ⭐⭐ | UCI |
| 8 | Wine Quality | Good vs bad wine classification | ⭐ | UCI |
🔵 Regression
| # | Dataset | Description | Level | Download |
|---|---|---|---|---|
| 1 | Boston Housing | Predict housing prices | ⭐ | Kaggle |
| 2 | California Housing | Median house values | ⭐ | Kaggle |
| 3 | House Prices | 79-variable price prediction | ⭐⭐ | Kaggle |
| 4 | Auto MPG | Fuel efficiency prediction | ⭐ | UCI |
| 5 | Student Performance | Exam score prediction | ⭐ | UCI |
| 6 | Medical Cost | Insurance charge prediction | ⭐ | Kaggle |
| 7 | Bike Sharing Demand | Hourly rental counts | ⭐⭐ | Kaggle |
🟡 NLP
| # | Dataset | Description | Level | Download |
|---|---|---|---|---|
| 1 | SMS Spam Collection | Spam vs ham classification | ⭐ | UCI |
| 2 | IMDB Reviews | 50K movie sentiment analysis | ⭐ | Kaggle |
| 3 | Twitter Airline Sentiment | Airline tweet classification | ⭐⭐ | Kaggle |
| 4 | Amazon Reviews | Multi-class sentiment | ⭐⭐ | Kaggle |
| 5 | Fake News | Real vs fake articles | ⭐⭐ | Kaggle |
| 6 | AG News | 4-topic news classification | ⭐⭐ | Hugging Face |
| 7 | Quora Question Pairs | Semantic equivalence detection | ⭐⭐⭐ | Kaggle |
🔴 Computer Vision
| # | Dataset | Description | Level | Download |
|---|---|---|---|---|
| 1 | MNIST | Handwritten digit classification | ⭐ | Yann LeCun |
| 2 | Fashion-MNIST | Clothing category classification | ⭐ | GitHub |
| 3 | CIFAR-10 | 10-class image classification | ⭐⭐ | Official |
| 4 | Dogs vs Cats | Binary image classification | ⭐⭐ | Kaggle |
| 5 | Flowers Recognition | 5 flower categories | ⭐ | Kaggle |
| 6 | Intel Image Classification | 6 natural scene categories | ⭐⭐ | Kaggle |
| 7 | Chest X-Ray Pneumonia | Pneumonia detection | ⭐⭐ | Kaggle |
🟣 Clustering / Unsupervised
| # | Dataset | Description | Level | Download |
|---|---|---|---|---|
| 1 | Mall Customers | Customer segmentation | ⭐ | Kaggle |
| 2 | Credit Card Segmentation | Spending behavior clusters | ⭐⭐ | Kaggle |
| 3 | Wholesale Customers | Distributor spending data | ⭐ | UCI |
| 4 | World Happiness Report | Country happiness scores | ⭐ | Kaggle |
| 5 | Online Retail | Market basket analysis | ⭐⭐ | UCI |
🟠 Time Series / Forecasting
| # | Dataset | Description | Level | Download |
|---|---|---|---|---|
| 1 | Air Passengers | Monthly airline counts (1949–1960) | ⭐ | GitHub |
| 2 | Stock Market Data | Historical stock prices | ⭐⭐ | Kaggle |
| 3 | COVID-19 (JHU) | Global case tracking | ⭐ | GitHub |
| 4 | Energy Consumption | Hourly power data (LSTM-ready) | ⭐⭐ | Kaggle |
| 5 | Jena Climate | 420K hourly weather readings | ⭐⭐⭐ | TensorFlow |
🔵 Recommendation Systems
| # | Dataset | Description | Level | Download |
|---|---|---|---|---|
| 1 | MovieLens (ml-100k) | 100K movie ratings | ⭐ | GroupLens |
| 2 | Book-Crossing | 270K+ book ratings | ⭐⭐ | Kaggle |
| 3 | Amazon Product Ratings | Cross-category ratings | ⭐⭐ | UCSD |
| 4 | Jester Jokes | 73K user joke ratings | ⭐⭐ | UC Berkeley |
🌐 Dataset Platforms
| Platform | Description | Link |
|---|---|---|
| Kaggle | Largest dataset community + competitions | kaggle.com/datasets |
| UCI ML Repository | Classic academic datasets | archive.ics.uci.edu |
| Google Dataset Search | Public dataset search engine | datasetsearch.research.google.com |
| Hugging Face | NLP datasets hub | huggingface.co/datasets |
| Papers With Code | Research-linked datasets | paperswithcode.com/datasets |
| data.gov | U.S. government open data | data.gov |
| OpenML | AutoML datasets | openml.org |
💡 Beginner tip: Start with Iris, Titanic, or MNIST — each teaches a complete ML workflow in under 100 lines of code!
⭐ Featured Projects
| 🧠 Healthcare | 💬 NLP & Chatbots | 📈 Forecasting |
| Alzheimer's Predictor | Chatbot Using RASA | COVID-19 Prophet Forecast |
| Brain Tumor Detection | Fake News Detection | Crude Oil Forecasting |
| Heart Disease Prediction | Emotion Recognition NLP | Covid Third Wave Forecast |
| 👁️ Computer Vision | 🤖 Deep Learning | 🏦 Finance & Business |
| Eye Gaze Tracking | ANN from Scratch | Portuguese Bank Marketing |
| Deepfake Image Analyzer | Bidirectional LSTM | Bitcoin Price Predictor |
| Chicken Disease Classification | Weapon Detection System | Customer Segmentation |
📂 Project Highlights
| # | Project | Description |
|---|---|---|
| 1 | Alzheimer's Disease Predictor | ML model predicting Alzheimer's likelihood using classification & feature selection |
| 2 | Chatbot Using RASA | Conversational AI handling diverse user queries |
| 3 | COVID-19 Forecasting with Prophet | Time-series forecasting of case trends |
| 4 | Fake News Detection | NLP-based fake news classification |
| 5 | Eye Gaze Tracking & Attention Estimation | Real-time attention tracking with MediaPipe & 3D head pose estimation |
| 6 | Portuguese Bank Marketing | Binary classification for term deposit subscription |
📂 All Projects
🔗 Browse All 500+ Projects on GitHub →
🔗 See it in website format →
📋 Quick Project Index — Click to expand full list
📌 & many more... Explore the full repository for 500+ additional projects!
🗂️ Projects by Domain
🟢 Beginner & Data Analysis
| Project | Category | Level | Link |
|---|---|---|---|
| Anime Data Analysis | Data Analysis | Beginner | View → |
| Medical Cost Prediction | ML | Beginner | View → |
| Heart Disease Detection | ML | Beginner | View → |
| Water Potability | ML | Intermediate | View → |
| Constellation Classification | ML | Intermediate | View → |
🔵 Deep Learning & Computer Vision
| Project | Category | Level | Link |
|---|---|---|---|
| Alzheimer's Predictor | Deep Learning | Intermediate | View → |
| Yoga Pose Detection | Computer Vision | Intermediate | View → |
| Speech-to-Image Generator | Deep Learning | Advanced | View → |
| Weapon Detection System | Computer Vision | Advanced | View → |
| Fashion Recommendation System | Deep Learning & Recommenders | Intermediate | View → |
🟡 NLP & Advanced ML
| Project | Category | Level | Link |
|---|---|---|---|
| Toxic Comment Classifier | NLP | Intermediate | View → |
| Currency Arbitrage with RL | Reinforcement Learning | Advanced | View → |
| Sales Prediction (Research) | ML | Advanced | View → |
| TweetMiner AI | NLP | Beginner | View → |
| Intelligent Network Intrusion Detection System | ML | Advanced | View → |
| Mental Health Risk Scorer | NLP & Transformers | Advanced | View → |
| Code Vulnerability Classifier | ML & Transformers | Advanced | View → |
🔗 Useful URLs
| Resource | Link |
|---|---|
| 8 Basic Statistics Concepts | KDnuggets |
| Machine Learning with Python (Coursera) | Coursera |
| Python ML Getting Started (W3Schools) | W3Schools |
| ML with Python (freeCodeCamp) | freeCodeCamp |
🚀 Get Started
# 1. Fork & clone the repository
git clone https://github.com/Niketkumardheeryan/ML-CaPsule.git
cd ML-CaPsule
# 2. Create a feature branch
git checkout -b my-feature
# 3. Pick a project folder and start learning!
🌟 Love this project? Give it a ⭐!
⚙️ Contribution Guidelines
We welcome contributions from everyone! Here's how to get involved:
| Step | Action | Link |
|---|---|---|
| 1 | Read contribution guidelines | CONTRIBUTING.md |
| 2 | Browse existing issues | Issues |
| 3 | Submit a pull request | Pull Requests |
Submitting a Pull Request
# Fork → Clone → Branch → Commit → Push → PR
git clone https://github.com/YOUR_USERNAME/ML-CaPsule.git
cd ML-CaPsule
git checkout -b my-feature
# Make your changes
git add .
git commit -m "Add: descriptive message about your change"
git push origin my-feature
Then open a Pull Request on GitHub with a clear description of your changes.
📁 Repository Structure & Architecture
ML-CaPsule/
├── 📂 .github/ # GitHub templates & workflows
│ ├── ISSUE_TEMPLATE/ # Standardized issue templates
│ ├── readme_template.md # Template for project READMEs
│ └── pullrequest_template.md # Template for submitting Pull Requests
├── 📂 Project Folders/ # 500+ self-contained ML/DL/NLP/CV projects
│ ├── 📂 / # Individual project folder (descriptive name)
│ │ ├── 📄 README.md # Project specific description, results & instructions
│ │ ├── 📄 *.ipynb / *.py # Implementation notebooks/scripts
│ │ ├── 📄 requirements.txt # (Optional) Folder-specific dependencies
│ │ └── 📂 assets / images # Screenshots & visual demonstrations
├── 📄 build_readme.py # Utility script for automated README index updating
├── 📄 CODE_OF_CONDUCT.md # Community behavioral standards
├── 📄 CONTRIBUTING.md # Detailed contribution workflow & coding guidelines
├── 📄 LICENSE # MIT License
├── 📄 README.md # Main repository overview & directory hub
└── 📄 ROADMAP.md # Structured 3-tier learning path (Beginner/Intermediate/Advanced)
🏗️ Architecture & Component Responsibilities
- Self-Contained ML Projects: Each directory represents an isolated Machine Learning workspace containing its own dataset loader, model training routine, analysis, visual outputs, and project README.
- Standardized Documentation Engine: All sub-projects conform to .github/readme_template.md to ensure consistent presentation of goals, models used, tech stack, and evaluation metrics.
- Automated Indexing:
build_readme.pyacts as a repository maintenance tool that dynamically updates project listings. - Structured Roadmap:
ROADMAP.mdorganizes projects into clear skill tiers (Beginner, Intermediate, Advanced) guiding new learners step by step.
Frequently Asked Questions & Troubleshooting
For the full troubleshooting guide covering installation, environment setup, runtime errors, and git workflow issues, see TROUBLESHOOTING.md.
Q1: How do I choose which project to start with?
Refer to ROADMAP.md which classifies projects into Beginner (foundational ML & EDA), Intermediate (Neural Networks, NLP, Web Apps), and Advanced (Computer Vision, Reinforcement Learning, Transformers).
Q2: What if a project has missing Python packages?
Ensure your virtual environment is active and install common data science packages:
pip install numpy pandas matplotlib seaborn scikit-learn jupyter
If the project folder contains a local requirements.txt, install it directly using:
pip install -r requirements.txt
Q3: Should I submit Jupyter Notebooks (.ipynb) or Python scripts (.py)?
Jupyter Notebooks (.ipynb) are preferred for project submissions as they combine code, visualization outputs, and explanations. Ensure all notebook cells are executed before committing.
📖 Code of Conduct
Please read our Code of Conduct before contributing.
✨ Contributors
Thanks to all these amazing people who made ML-CaPsule possible! 🎉
Niket Kumar Dheeryan
Author 💻
❤️ Stargazers & Forkers
⭐ Stargazers
🍴 Forkers
📝 License
This project is licensed under the MIT License.
Happy Coding! 👩💻👨💻
Feel free to create issues, fix bugs, and contribute. Join our community!
Built with ❤️ by Niketkumardheeryan and contributors
🧭 Suggested Learning Path — Click to expand
Start with beginner-friendly projects, then move to intermediate and advanced ones as your confidence grows. The table below adds quick difficulty and time guidance for each project.