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🧠 ML Algorithms — Core Machine Learning, Deep Learning & Reinforcement Learning Implementations

A comprehensive collection of Machine Learning, Deep Learning, and Reinforcement Learning algorithms implemented from scratch with Python. This repository focuses on learning algorithm logic, building core intuition, and understanding how ML systems work behind the scenes.

🚀 Overview

This project contains implementations of classic and modern ML, DL, and RL algorithms — each written to demonstrate the internal mechanics, not just library usage.

This repo is ideal for: ✔ Learning machine learning fundamentals ✔ Strengthening algorithm intuition ✔ Preparing for ML interviews ✔ Academic reference and study

📚 Algorithms Included

📌 Supervised Learning

Regression

  • linear_regression.py — Linear Regression
  • Polynomial_regression.py — Polynomial Regression
  • Ridge_lasoo_regression.py — Ridge & Lasso Regression
  • Gradient_bosting.py — Gradient Boosting Regression

Classification

  • logistic_regression.py — Logistic Regression
  • knn.py, k_nearest_neightbours.py — K-Nearest Neighbors
  • SVM.py — Support Vector Machines
  • decision_trees.py — Decision Tree Classifier
  • Random_forest.py — Random Forest Classifier

📌 Unsupervised Learning

  • k_means_clustering.py — K-Means Clustering
  • Hierarchical_clustering.py — Hierarchical Clustering
  • dbscan.py — DBSCAN Clustering
  • Gausian_mixture.py — Gaussian Mixture Models

📌 Dimensionality Reduction & Visualization

  • Principal_component_analysis.py — PCA
  • TSNE.py — t-SNE

📌 Deep Learning

  • cnn.py — Convolutional Neural Network
  • rnn.py — Recurrent Neural Network
  • lstm.py — LSTM Network
  • Autoencoders.py — Autoencoder
  • Self_training_encoders.py — Self-Training Encoders
  • transformers.py — Transformer Architecture

📌 Reinforcement Learning

  • QLearning.py — Q-Learning
  • DeepQNetworks.py — Deep Q Networks
  • PolicyGradient.py — Policy Gradient Methods

📌 Utility & Extras

  • native_bytes.py — Misc utility example
  • main.py — Example runner script (entry point pattern)

🧠 Learning Approach

This repository emphasizes: ✅ Algorithm fundamentals — how they work ✅ Python implementations from scratch ✅ Exposure to multiple ML paradigms ✅ Minimal reliance on black-box libraries ✅ Clear progression from basic → advanced

📦 Tech Stack

Category Technology
Language Python 🐍
Libraries Used Numpy, Pandas, Scikit-learn (optional), Matplotlib (optional visualization)
Focus ML / DL / RL fundamentals
Repository Type Educational + Reference