machine-learning-projects 🚀🤖

Machine learning projects from Grokking Machine Learning by Luis Serrano

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🌟 About This Project

Welcome! This repository is a hands-on journey through the most important concepts in machine learning, with code, datasets, and visualizations for each chapter. Each notebook is designed for interactive learning and experimentation.

📚 What You'll Learn

• Linear Regression: Predicting housing prices and visualizing regression lines
• Overfitting & Underfitting: How to test, regularize, and improve models
• Perceptron Algorithm: Sentiment analysis and binary classification
• Logistic Regression: Sentiment analysis with probabilistic models
• Naive Bayes: Text classification and probability-based predictions
• Decision Trees: App recommendations and interpretable models
• Neural Networks: Deep learning for house price prediction and image recognition
• Support Vector Machines (SVM): Building datasets, visualizing boundaries, and kernel tricks
• Ensemble Methods: AdaBoost, Random Forests, Gradient Boosting, and XGBoost for robust predictions
• End-to-End Example: Full machine learning pipeline on the Titanic dataset
• Unsupervised Learning: Image compression and clustering

🆕 New Features & Additions

• Expanded chapters on SVM, Ensemble Methods, and End-to-End ML pipelines
• More datasets for experimentation
• Advanced visualizations and interactive widgets
• Step-by-step explanations for each algorithm
• Real-world case studies and projects
• Tips for tuning and evaluating models

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📂 Project Structure

• Chapter 03: Linear Regression
  • README
  • Predicting housing prices with linear regression
• Chapter 04: Testing Overfitting & Underfitting
  • README
  • Regularization, train/test split, and model evaluation
• Chapter 05: Perceptron Algorithm
  • Sentiment analysis using the perceptron algorithm
• Chapter 06: Logistic Regression
  • Sentiment analysis with logistic regression
• Chapter 08: Naive Bayes
  • Text classification using Naive Bayes
• Chapter 09: Decision Trees
  • App recommendations and decision tree visualizations
• Chapter 10: Neural Networks
  • House price prediction and image recognition with neural networks
• Chapter 11: Support Vector Machines (SVM)
  • Building datasets, visualizing SVM boundaries, and kernel tricks
• Chapter 12: Ensemble Methods
  • AdaBoost, Random Forests, Gradient Boosting, and XGBoost
• Chapter 13: End-to-End Example
  • Full ML pipeline on the Titanic dataset
• Unsupervised Learning
  • Image compression and clustering

Each chapter folder contains Jupyter notebooks and relevant datasets. For more details, see the README in each chapter (where available).

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📊 Example Visualizations

Linear Regression	Decision Tree	Neural Network

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🚦 Getting Started

1. Clone the repository
2. Open any chapter folder and launch the Jupyter notebook
3. Follow the code and explanations to learn each concept

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📜 License

MIT License