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This bestselling book uses concrete examples, minimal theory, and production-ready Python frameworks (Scikit-Learn, Keras, and TensorFlow) to help you gain an intuitive understanding of the concepts and tools for building intelligent systems.
Item #:
53210962
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3e: Concepts, Tools, and Techniques to Build Intelligent Systems
87% of respondents would recommend this to a friend
Item #:
53210962
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This bestselling book uses concrete examples, minimal theory, and production-ready Python frameworks (Scikit-Learn, Keras, and TensorFlow) to help you gain an intuitive understanding of the concepts and tools for building intelligent systems.
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What Stands Out
Comprehensive Coverage
This book offers an in-depth exploration of essential machine learning concepts, tools, and techniques, making it suitable for beginners and experienced practitioners alike looking to build intelligent systems.
Hands-On Approach
With practical examples and exercises, readers gain hands-on experience in applying Scikit-Learn, Keras, and TensorFlow, ensuring they can effectively implement machine learning projects in real-world scenarios.
Up-to-Date Content
Published in November 2022, this book incorporates the latest advancements in machine learning, providing readers with current strategies and practices that reflect today's rapidly evolving technology landscape.
Product Details
| Publisher | O'Reilly Media |
| Publication date | 31 Oct. 2022 |
| Edition | 3rd |
| Language | English |
| Print length | 861 pages |
| ISBN-10 | 1098125975 |
| ISBN-13 | 978-1098125974 |
| Item weight | 1.36 kg |
| Dimensions | 18.42 x 5.08 x 24.13 cm |
Who Should Buy?
-
Beginner Learners
Newcomers to machine learning who want a comprehensive introduction with practical examples and clear explanations will benefit greatly.
-
Data Scientists
Professionals looking to enhance their practical skills in building machine learning models using popular libraries like TensorFlow and Keras.
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Computer Science Students
University students studying machine learning concepts who need a resourceful guide with hands-on coding exercises and projects.
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Advanced Practitioners
Experienced data scientists may find the content too basic and lacking in depth for their advanced needs.
Product Description
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3e: Concepts, Tools, and Techniques to Build Intelligent Systems
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AI & Machine Learning Editorial Review
Hands-On Machine Learning With Scikit-Learn, Keras, And TensorFlow 3e: Concepts, Tools, And Techniques To Build Intelligent Systems is an essential guide for anyone looking to dive into the world of machine learning. This comprehensive text, published by O'Reilly Media, spans an impressive 861 pages filled with valuable insights and practical examples that enhance understanding of foundational concepts, algorithms, and model implementations. Readers have noted that Geron's clear writing and systematic approach make it suitable for both beginners and experienced practitioners, leading to a deep grasp of machine learning techniques in Python. While some have mentioned that the book may become dated with the rapid advancement in tools like Keras and TensorFlow, the fundamental knowledge it provides is deemed invaluable for future applications in the field.
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Pros
- In-depth explanations of foundational concepts
- Covers a wide range of machine learning topics
- Clear writing style for better understanding
- Includes practical applications and code samples
- Highly recommended for both beginners and experts
Cons
- Some content may become dated with tool updates
Product Price History
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Features & Benefits
- Book explores machine learning techniques using Scikit-learn, Keras and TensorFlow
- Suitable for programmers with no prior experience in deep learning
- Covers a range of models from simple linear regression to deep neural networks
- Includes code examples and exercises throughout the book
- Dives into neural net architectures for computer vision, NLP, generative models, deep reinforcement learning
- Explores unsupervised learning techniques like clustering, dimensionality reduction and anomaly detection
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