This repository is dedicated to documenting my learning journey through the book "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron. The repository contains my personal notes, code examples, and projects for each chapter of the book, along with any additional resources or insights that I find helpful along the way.
The repository is organized into separate notebooks for each chapter of the book, with a brief description of the chapter's content. Each folder contains the relevant code and notes for that chapter.
- Chapter01_Introduction
- Chapter02_End_to_End_ML_Project
- Chapter03_Classification
- Chapter04_Training_Models
- Chapter05_Support_Vector_Machines
- Chapter06_Decision_Trees
- Chapter07_Ensemble_Learning_and_Random_Forests
- Chapter08_Dimensionality_Reduction
- Chapter09_Up_and_Running_with_TensorFlow
- Chapter10_Introduction_to_Artificial_Neural_Networks
- Chapter11_Training_Deep_Neural_Networks
- Chapter12_Custom_Models_and_Training_with_TensorFlow
- Chapter13_Loading_and_Preprocessing_Data_with_TensorFlow
- Chapter14_Deep_Computer_Vision_Using_Convolutional_Neural_Networks
- Chapter15_Processing_Sequences_Using_RNNs_and_CNNs
- Chapter16_NLP_with_RNNs_and_Attention
- Chapter17_Generative_Adversarial_Networks
- Chapter18_Reinforcement_Learning
- Chapter19_Training_and_Deploying_TensorFlow_Models_at_Scale
"Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" is a practical guide to machine learning and deep learning techniques using popular Python libraries like Scikit-Learn, TensorFlow, and Keras. The book covers a wide range of topics, from basic ML concepts and algorithms to advanced deep learning techniques, providing a comprehensive understanding of the field.
Some of the key topics covered in the book include:
- Supervised and unsupervised learning
- Feature engineering and model evaluation
- Linear regression, logistic regression, decision trees, and support vector machines
- Neural networks and deep learning, including CNNs, RNNs, and attention mechanisms
- TensorFlow and Keras for creating custom models, layers, and training
- Regularization, dropout, and batch normalization techniques for improving models
- Dimensionality reduction and ensemble learning methods
- Reinforcement learning and generative adversarial networks (GANs)
Géron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (2nd ed.). O'Reilly Media.
This repository is for educational purposes only. All code and materials are provided under the MIT License.