Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

by Aurélien Géron

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow is a book by Aurélien Géron that provides a practical introduction to machine learning using Python and its popular libraries, including scikit-learn, Keras, and TensorFlow. The book is designed to be accessible to readers with a background in programming, but no prior experience in machine learning.

The book is organized into three main parts. Part I, “The Fundamentals of Machine Learning,” provides an introduction to the field of machine learning and covers the key concepts and techniques that are used in the field. Part II, “Training Machine Learning Algorithms for Classification,” covers a range of supervised learning algorithms that are used for classification tasks, including decision trees, support vector machines, and neural networks. Part III, “Training Machine Learning Algorithms for Regression,” covers a range of supervised learning algorithms that are used for regression tasks, including linear regression, polynomial regression, and kernel regression.

Throughout the book, the author provides clear and intuitive explanations of the key concepts and techniques in machine learning, and illustrates their use with a wide range of examples and exercises. The book also includes a number of case studies that demonstrate the application of machine learning in real-world settings.

Table of Contents

The book is organized into the following chapters:

Part I: The Fundamentals of Machine Learning

1. Introduction
2. End-to-End Machine Learning Project
3. Classification
4. Training Models
5. Support Vector Machines
6. Decision Trees
7. Ensemble Learning and Random Forests
8. Dimensionality Reduction
9. Unsupervised Learning Techniques

Part II: Training Machine Learning Algorithms for Classification 10. Introduction to Artificial Neural Networks

11. Training Deep Neural Networks
12. Fine-Tuning Neural Network Hyperparameters
13. Building Custom Neural Network Components in Keras
14. Running Deep Learning Workflows on Multiple GPUs Using TensorFlow

Part III: Training Machine Learning Algorithms for Regression 15. Introduction to Regression

16. Training Linear Regression Models
17. Training Nonlinear Regression Models
18. Training Polynomial Regression Models
19. Training Kernel Regression Models

In addition to the main chapters, the book also includes a number of appendices that provide additional information and resources for readers, as well as a wide range of exercises and programming projects to help readers develop a deeper understanding of the material.

Main takeaways

Here are three main takeaways from “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron:

1. The book provides a practical introduction to machine learning using Python and its popular libraries, including scikit-learn, Keras, and TensorFlow. It is suitable for readers with a background in programming, but no prior experience in machine learning.
2. The book covers a wide range of machine learning techniques, including supervised and unsupervised learning, dimensionality reduction, and deep learning. It illustrates the key concepts and techniques with a wide range of examples and exercises.
3. The book includes a number of case studies that demonstrate the application of machine learning in real-world settings. It also includes a number of appendices that provide additional information and resources for readers, as well as a wide range of exercises and programming projects to help readers develop a deeper understanding of the material.

Conclusion

Overall, I would say that Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow is a relatively accessible and practical introduction to machine learning and deep learning, and would be suitable for readers who are looking to get started in these fields.

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