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Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
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Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
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.
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
商品＃: 51836933

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

87% of respondents would recommend this to a friend

商品＃: 51836933

TWD 2289

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立即訂購並活動它 週三, 十月 07
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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 extensively covers essential machine learning concepts, tools, and techniques, making it a valuable resource for both beginners and experienced practitioners aiming to build intelligent systems.
Hands-On Approach
The practical, hands-on approach allows readers to implement real-world projects using Scikit-Learn, Keras, and TensorFlow, enhancing understanding and retention of complex machine learning concepts.
Updated Content
The third edition includes the latest advancements in machine learning and deep learning, ensuring readers are equipped with current knowledge and skills relevant to today's rapidly evolving technology landscape.

產品詳情

Get the latest edition of Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. Enhance your knowledge and build intelligent systems. Shop now at Ubuy Taiwan.
  • Book designed for readers with Python programming experience and familiarity with main scientific libraries
  • Suitable for those interested in machine learning applications such as customer segmentation, product recommendation, fraud detection, and revenue forecasting
  • Intended to provide concepts, tools, and intuition needed to implement programs capable of learning from data
  • Covers a wide range of techniques, from simple to deep learning algorithms, utilizing production-ready Python frameworks like Scikit-Learn, TensorFlow, and Keras
  • Scikit-Learn is user-friendly and implements many machine learning algorithms efficiently, making it a great entry point to learning machine learning
  • TensorFlow and Keras offer more complex and high-level deep learning capabilities, with TensorFlow supporting large-scale machine learning applications
Package Weight3.3 Pound

Who Should Buy?

Suitable For
  • Aspiring Data Scientists

    Perfect for beginners looking to build a strong foundation in machine learning concepts and techniques.

  • Developers and Programmers

    Ideal for professionals wanting to integrate machine learning into applications using popular frameworks like TensorFlow and Keras.

  • Machine Learning Enthusiasts

    Great for hobbyists eager to explore practical machine learning projects and hands-on experiments.

Not Suitable For
  • Complete Beginners

    Not suitable for those with no prior programming or mathematical knowledge, as it assumes basic familiarity.

產品描述

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

有疑問？ 和我們聊天

客戶問題與解答

  • 問題: What topics are covered in 'Hands-On Machine Learning With Scikit-Learn, Keras, And TensorFlow'?

    Answer: This book provides an extensive overview of essential machine learning concepts, algorithms, and tools. It covers Scikit-Learn for traditional ML techniques, Keras for building neural networks, and TensorFlow for deep learning and complex model implementations. Readers will explore practical applications, such as image classification, natural language processing, and regression analysis, equipping them with the knowledge to build robust and intelligent systems.
  • 問題: Who is this book intended for?

    Answer: The book is designed for aspiring data scientists, software engineers, and machine learning enthusiasts. Whether you’re a beginner seeking foundational knowledge or an intermediate learner wanting to deepen your understanding of deep learning frameworks, this resource is suitable for you. Practical examples and hands-on projects cater to various skill levels, making it a valuable asset for anyone wanting to excel in machine learning.
  • 問題: What programming language is primarily used in this book?

    Answer: This book predominantly uses Python, which is the most popular programming language in machine learning and data science. Python’s simplicity and readability make it an ideal choice for educators and practitioners alike. The book demonstrates how to implement machine learning techniques using Python libraries, allowing readers to quickly learn to use these powerful tools in their projects.
  • 問題: Are there practical projects included in the book?

    Answer: Yes, the book features several practical projects that guide you through real-world applications of machine learning. These projects include hands-on coding exercises where you can apply what you've learned, such as building models for image recognition and predictive analysis. Engaging in these projects helps reinforce theoretical knowledge while providing an opportunity to solve actual problems using machine learning.
  • 問題: How does this book differ from other machine learning resources?

    Answer: This book stands out due to its focus on practical implementation alongside theoretical knowledge. While many resources only cover concepts, this book encourages readers to engage in hands-on exercises using popular frameworks. Its structured approach combines theory, coding, and real-world scenarios, providing a well-rounded learning experience that equips you with both knowledge and practical skills.
  • 問題: Is prior knowledge of machine learning required to read this book?

    Answer: No prior knowledge is strictly required to read this book. It begins with the basics of machine learning and gradually progresses to more advanced topics. However, familiarity with Python programming is beneficial, as it allows readers to follow along with code examples and exercises effectively. The book is designed to make machine learning accessible to beginners while still being informative for those with some background knowledge.
  • 問題: Can this book help me prepare for a career in data science?

    Answer: Absolutely! This book serves as a comprehensive resource that equips you with the necessary skills for a career in data science. By covering essential machine learning techniques and providing hands-on experience, it prepares you for tasks commonly faced in data science roles, such as model development, data analysis, and algorithm implementation. Completing the projects will bolster your portfolio, showcasing your practical skills to potential employers.
  • 問題: What kind of challenges or exercises does the book provide?

    Answer: The book includes numerous challenges and exercises at the end of each chapter, designed to reinforce key concepts. These challenges vary in difficulty and encourage creative problem-solving using the tools and techniques covered in the book. Completing these exercises not only solidifies your understanding but also prepares you to tackle real-world data science problems effectively.
  • 問題: Is there a companion website or additional resources available for this book?

    Answer: Yes, the author often provides supplementary materials on a companion website or a dedicated GitHub repository. These resources may include code examples, datasets, and updates to keep the content current. Access to additional resources enhances the learning experience, allowing readers to delve deeper into topics and have practical support as they work on their projects.
  • 問題: Where can I buy Hands-On Machine Learning With Scikit-Learn, Keras, And TensorFlow in Taiwan?

    Answer: You can purchase 'Hands-On Machine Learning With Scikit-Learn, Keras, And TensorFlow' at Ubuy, which offers a comprehensive shopping experience with a wide selection of books. Ubuy provides detailed product descriptions, user reviews, and various purchasing options tailored to customers in Taiwan. By shopping at Ubuy, you can ensure that you receive your desired book efficiently and conveniently.

Intelligence & Semantics Editorial Review

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems offers a comprehensive approach to understanding machine learning techniques through practical exercises. Readers have praised the book for its clear writing style and the balance it strikes between theoretical concepts and practical application. One of the notable features is the detailed exercises that challenge learners, fostering a deeper understanding of the concepts. While some reviewers noted the focus on Keras and TensorFlow, they found that knowledge of one framework eases the transition to others. The book is filled with helpful visual aids and comprehensive explanations, making it an excellent resource for both beginners and those looking to enhance their skills in machine learning.

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優點

  • Clear writing style enhances reader engagement
  • Thorough exercises for deep learning
  • Helpful visual aids throughout the text
  • Comprehensive reference for algorithms
  • Balanced approach between theory and application

缺點

  • Book's paper quality could be improved for durability

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TWD 2289

立即訂購並活動它 週三, 十月 07

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特色和優勢

  • 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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