Hands-On Machine Learning (3rd Edition) Practices: This repository features my comprehensive solutions and implementations from Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (3rd Edition) by Aurélien Géron. Through these practices, I explore machine learning and deep learning concepts, applying them to real-world datasets and scenarios. Each chapter covers essential ML and DL methods, reinforced with code, exercises, and projects.
Repository Structure
- Chapter-Wise Code & Notebooks: Each chapter is broken down into its own directory containing Python notebooks and code scripts that match the book's organization. Notebooks include both code implementations and additional explanations to reinforce learning.
- Additional Notes and Summaries: Each chapter contains a summary of key takeaways, concepts, and algorithms, along with any clarifications or insights that go beyond the book to provide additional context.
- Exercises and Solutions: Solved exercises are included at the end of each chapter’s folder. Some sections also contain expanded solutions, and projects based on real-world datasets for further exploration.
Key Topics Covered:
- Data Preprocessing & Analysis: Techniques to clean, transform, and analyze data to make it ready for machine learning models.
- Supervised & Unsupervised Learning: Implementation of popular algorithms like regression, classification, clustering, and dimensionality reduction.
- Neural Networks and Deep Learning: Hands-on exploration of neural network architectures, activation functions, optimizers, and more using Keras and TensorFlow.
- Model Evaluation & Optimization: Methods for evaluating models and optimizing their performance with techniques like cross-validation, grid search, and hyperparameter tuning.
- Advanced Concepts: Practical introduction to reinforcement learning, natural language processing, generative models, and transfer learning.
Getting Started:
- Clone this repository to access the content.
- Install the required libraries as listed in each chapter’s notebook or requirements.txt.
- Explore and Run the Notebooks to follow along with each chapter or work through exercises on your own.
Why This Repository?
- This repository serves as a learning companion and resource for both beginners and advanced learners looking to deepen their understanding of machine learning. It can also be used as a reference for implementing core machine learning techniques across a variety of domains.