🚀 Complete Machine Learning Mastery

> "The best time to plant a tree was 20 years ago. The second best time is now."
> My structured journey from ML fundamentals to advanced implementations.

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📖 Table of Contents

• About
• Learning Roadmap
• Repository Structure
• Setup & Installation
• Progress Tracking
• Resources
• Contributing
• License

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🎯 About

This repository documents my systematic journey to master machine learning from the ground up. Here you'll find:

• 📚 Theory & Notes: Structured notes on ML concepts, mathematics, and algorithms
• 💻 Code Implementations: From scratch implementations and practical projects
• 📊 Datasets: Curated datasets for practice
• 🧪 Experiments: Jupyter notebooks with model comparisons and ablations
• 📝 Learning Log: Weekly progress updates and reflections

Goal: Build intuitive understanding + practical skills + production-ready projects

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🗺️ Learning Roadmap

Phase 1: Foundations (Weeks 1-6)

• Mathematics: Linear Algebra, Calculus, Probability & Statistics
• Python: NumPy, Pandas, Matplotlib/Seaborn
• Data Preprocessing: Cleaning, feature engineering, EDA

Phase 2: Classical ML (Weeks 7-12)

• Supervised Learning: Regression, Classification, Decision Trees, SVMs
• Unsupervised Learning: Clustering, Dimensionality Reduction
• Ensemble Methods: Bagging, Boosting, Random Forest, XGBoost
• Model Evaluation: Cross-validation, metrics, bias-variance tradeoff

Phase 3: Deep Learning (Weeks 13-20)

• Neural Networks: MLP, Backpropagation, Activation Functions
• CNNs: Image classification, Transfer Learning
• RNNs/LSTMs: Sequence modeling, Time series
• Transformers: Attention mechanism, BERT, GPT

Phase 4: Advanced Topics (Weeks 21-30)

• Computer Vision: Object detection, Segmentation, GANs
• NLP: Text classification, NER, Generation
• MLOps: Model deployment, Docker, Cloud platforms
• Specialized Topics: RL, GNNs, Federated Learning

Phase 5: Real-World Projects (Ongoing)

• Kaggle Competitions: 5+ competitions with top 10% scores
• Full-Stack Projects: 3+ deployed ML applications
• Research Paper: 1 reproduced paper with improvements

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📁 Repository Structure