🧠 Machine Learning Repository

Welcome to my Machine Learning Learning Journey! 🚀

This repository documents my progress — from the mathematical foundations of ML algorithms to full-fledged implementations. It includes from-scratch derivations, Scikit-learn applications, and clear visualizations for better understanding.

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📘 Repository Structure

🔹 Core Chapters

• Chapter1, Chapter2, Chapter3 — Foundational concepts, theory, and introductory experiments.

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📊 Regression Algorithms

• Linear Regression
  • Implemented using Normal Equation, Scikit-learn, and custom Gradient Descent.
• Multiple Linear Regression
  • Extension of Linear Regression with multiple features.
• Polynomial Regression
  • Demonstrating underfitting vs. overfitting.
• Ridge Regression, Lasso, Elastic Net
  • Regularization techniques to handle multicollinearity and prevent overfitting.

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🧮 Optimization Techniques

• Gradient Descent
  • Batch, Stochastic, and Mini-Batch implementations.
  • Detailed analysis of convergence and learning rates.

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🎯 Classification Algorithms

• Logistic Regression
  • Binary and multiclass classification.
• Naive Bayes
  • Probabilistic approach for categorical and text data.
• Support Vector Machines (SVMs)
  • Linear and RBF kernel-based models.
• Decision Trees
  • Visualization and interpretability.
• PCA (Principal Component Analysis)
  • Dimensionality reduction and visualization of feature spaces.

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🌲 Ensemble Learning

• Bagging Ensemble
  • Combining weak learners to reduce variance.
• Voting Ensemble
  • Hard and soft voting classifiers.
• Gradient Boosting
  • Step-by-step implementation on the Iris dataset (gradient-boosting-classifier-on-iris.ipynb).
• (Upcoming) AdaBoost, XGBoost, and Random Forests.

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💡 Key Concepts Covered

• Data Preprocessing: Scaling, encoding, handling missing values.
• Model Evaluation: R², Accuracy, Precision, Recall, F1-score, ROC-AUC.
• Bias–Variance Tradeoff: Theory and practical illustrations.
• Regularization & Optimization: Ridge, Lasso, Elastic Net, Gradient Descent.
• Dimensionality Reduction & Feature Selection.

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🛠️ Technologies Used

• Language: Python 🐍
• Libraries:
  NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, mlxtend, and TensorFlow (for datasets)

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📚 Learning Focus

Each folder contains:

• Jupyter notebooks explaining the math intuition 👨‍🏫
• Implementation from scratch and via Scikit-learn ⚙️
• Visualizations and model evaluations 📈

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