Title
Handson Machine Learning With Scikitlearn, Keras, And Tensorflow: Concepts, Tools, And Techniques To Build Intelligent Systems,New
Processing time: 1-3 days
US Orders Ships in: 3-5 days
International Orders Ships in: 8-12 days
Return Policy: 15-days return on defective items
Payment Option
Through a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This bestselling book uses concrete examples, minimal theory, and productionready Python frameworks (ScikitLearn, Keras, and TensorFlow) to help you gain an intuitive understanding of the concepts and tools for building intelligent systems.With this updated third edition, author Aurlien Gron explores a range of techniques, starting with simple linear regression and progressing to deep neural networks. Numerous code examples and exercises throughout the book help you apply what you've learned. Programming experience is all you need to get started.Use Scikitlearn to track an example ML project end to end Explore several models, including support vector machines, decision trees, random forests, and ensemble methods Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning
⚠️ WARNING (California Proposition 65):
This product may contain chemicals known to the State of California to cause cancer, birth defects, or other reproductive harm.
For more information, please visit www.P65Warnings.ca.gov.
- Q: How many pages does this book have? A: This book has eight hundred sixty-one pages. It provides extensive content on machine learning concepts and practical applications.
- Q: What type of binding does this book have? A: This book is paperback bound. This makes it lightweight and easy to handle while reading.
- Q: What is the size of this book? A: The book measures seven point twenty-five inches in length, two point zero one inches in width, and nine point five inches in height. Its dimensions are suitable for easy storage and portability.
- Q: How do I start using the concepts in this book? A: Begin by following the concrete examples provided in the text. Each chapter is designed to build your understanding progressively.
- Q: Is this book suitable for beginners? A: Yes, this book is suitable for beginners with basic programming experience. It is written to guide readers step-by-step through machine learning techniques.
- Q: What programming experience do I need to read this book? A: You only need basic programming experience to get started. The book is designed to be accessible even to those new to machine learning.
- Q: How should I store this book? A: Store this book in a cool, dry place to prevent damage. Keeping it upright on a shelf will help maintain its condition.
- Q: Is this book safe for young readers? A: Yes, the content is educational and focused on machine learning. However, it is recommended for readers with some programming knowledge.
- Q: Can I return this book if I change my mind? A: Yes, you can return the book according to the retailer's return policy. Be sure to check for any specific conditions for returns.
- Q: What if the book arrives damaged? A: If the book arrives damaged, contact the retailer for a replacement or return. Most retailers have clear policies for such situations.
- Q: What topics does this book cover? A: This book covers various topics, including linear regression, neural networks, and unsupervised learning techniques. It provides a comprehensive guide to machine learning.
- Q: Does this book include code examples? A: Yes, the book includes numerous code examples throughout. These examples help illustrate the concepts discussed in each chapter.
- Q: What kind of projects can I learn to implement? A: You can learn to implement a range of projects, from simple models to complex neural networks for different applications. The book emphasizes practical learning.
- Q: Is there a focus on any specific programming frameworks? A: Yes, the book focuses on Scikit-Learn, Keras, and TensorFlow. These frameworks are essential tools for implementing machine learning algorithms.
- Q: Are there exercises in this book? A: Yes, the book contains exercises designed to reinforce learning. These exercises allow you to apply the concepts you learn in practical scenarios.