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Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3e: Concepts, Tools, and Techniques to Build Intelligent Systems
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Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3e: 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 3e: Concepts, Tools, and Techniques to Build Intelligent Systems
Item #: 53210962

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3e: Concepts, Tools, and Techniques to Build Intelligent Systems

Item #: 53210962

PKR 24505

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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

Get your hands on the latest edition of the bestselling book to learn concepts, tools, and techniques to build intelligent systems. Paperback available - 31 Oct. 2022. Shop at Ubuy Pakistan
Item Weight1.5 lbs (680 grams)

Who Should Buy?

Suitable For
  • 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.

  • Computer Science Students

    University students studying machine learning concepts who need a resourceful guide with hands-on coding exercises and projects.

Not Suitable For
  • Advanced Practitioners

    Experienced data scientists may find the content too basic and lacking in depth for their advanced needs.

  • Casual Readers

    Individuals seeking light reading or non-technical content may find the technical details and hands-on approach overwhelming.

  • Non-Technical Users

    Users without a background in programming or data analysis will struggle to grasp the book's concepts and exercises.

Product Description

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3e: Concepts, Tools, and Techniques to Build Intelligent Systems

Have any Query? Chat with us

Customer Questions & Answers

  • Question: What is Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow?

    Answer: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow is a comprehensive guide that provides practical techniques and concepts for building intelligent systems using popular Python libraries. The book covers fundamental machine learning concepts alongside practical coding examples, focusing on using Scikit-Learn and TensorFlow for real-world applications.
    Category: overview
    Confidence: high
  • Question: What edition is the latest for Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow?

    Answer: The latest edition available is the 3rd edition of Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. It includes updated content to reflect the latest developments in the field of machine learning and improvements in the libraries covered.
    Category: overview
    Confidence: high
  • Question: What are the prerequisites for reading this book?

    Answer: Readers should have a basic understanding of Python programming and familiarity with basic linear algebra and statistics. Prior knowledge of machine learning concepts is beneficial but not strictly necessary. The book is designed to be accessible for beginners while also providing depth for more advanced practitioners.
    Category: setup
    Confidence: high
  • Question: What topics are covered in the book?

    Answer: This book covers a wide range of topics including supervised and unsupervised learning, neural networks, deep learning, model evaluation, and deployment techniques. It also discusses practical applications and how to leverage Keras and TensorFlow for building machine learning models.
    Category: overview
    Confidence: high
  • Question: Are there any software requirements to follow along with the book?

    Answer: Yes, readers will need to have Python installed along with libraries like Scikit-Learn, Keras, and TensorFlow. Specific installation instructions and requirements are typically provided in the introductory chapters of the book.
    Category: setup
    Confidence: high
  • Question: How is the book structured?

    Answer: The book is structured in a way that begins with foundational concepts and gradually introduces more complex topics. Each chapter includes hands-on exercises and examples to reinforce learning, along with illustrations and code snippets for clarity.
    Category: overview
    Confidence: high
  • Question: What is the target audience for this book?

    Answer: The target audience includes students, educators, and professionals looking to enter the field of machine learning or enhance their skills. It is suited for both novices seeking to learn machine learning and experienced practitioners looking to update their knowledge with the latest tools and techniques.
    Category: overview
    Confidence: high
  • Question: Can you provide details about the author's background?

    Answer: The author, Aurélien Géron, is an experienced consultant and educator in machine learning. He has worked extensively with various companies and possesses a deep understanding of applied machine learning. His expertise is reflected throughout the book, offering practical insights.
    Category: overview
    Confidence: high
  • Question: Is there a warranty or return policy for the book?

    Answer: Refunds and returns for books typically depend on the retailer's policy. Most retailers allow returns if the book is in new condition and within a specified time frame, usually 30 days. It's recommended to check the specific return policy of the retailer where the purchase is made.
    Category: returns
    Confidence: medium
  • Question: What kinds of exercises are included?

    Answer: The book includes a variety of exercises ranging from coding projects to practical case studies, allowing readers to reinforce their understanding of machine learning concepts through hands-on practice. These exercises help apply the theoretical knowledge gained in each chapter.
    Category: usage
    Confidence: high
  • Question: Does the book cover any online resources or tools?

    Answer: Yes, the book often references online resources, including Jupyter notebooks and online courses that complement the learning experience. These resources provide additional materials for readers to practice and experiment with the concepts discussed in the book.
    Category: software
    Confidence: medium
  • Question: Are there any differences between the 3rd edition and earlier editions?

    Answer: The 3rd edition includes updates reflecting the latest changes in libraries like TensorFlow and Keras, improved examples, and additional topics that were not present in earlier editions. These enhancements aim to provide readers with current best practices in machine learning.
    Category: other
    Confidence: high
  • Question: What are the safety considerations when using machine learning technologies discussed in the book?

    Answer: Safety considerations generally include ensuring data privacy, understanding algorithm biases, and implementing security measures to protect model integrity. The book discusses broader ethical implications of machine learning applications in various industries.
    Category: safety
    Confidence: medium

AI & Machine Learning Editorial Review

The "Hands–On Machine Learning with Scikit–Learn, Keras, and TensorFlow 3e: Concepts, Tools, and Techniques to Build Intelligent Systems" book has received a mixture of positive and negative feedback. The majority of the reviews praise the book for its comprehensive and practical approach to machine learning and AI. Customers appreciate the practical examples and the high density of information, with one reviewer even calling it a masterpiece. The book is described as a substantial and invaluable resource for anyone working on machine learning projects. Clear explanations and working code examples are also highlighted as key positives. However, there are also complaints about the packaging and protection of the book during delivery, as well as the overwhelming amount of content, which some found challenging to retain. A potential need for supplementary resources or further reading is also mentioned.

Customer Reviews & Ratings

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Pros

  • Comprehensive and practical approach to machine learning and AI
  • High density of information
  • Substantial and invaluable resource for machine learning projects
  • Clear explanations and working code examples

Cons

  • Overwhelming amount of content

Product Price History

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PKR 24505

Order now and get it around Thursday, July 02

QTY:

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Ubuy works hard to protect your security and privacy. Our advanced payment security system ensures confidentiality by encrypting your information during transmission using AES (Advanced Encryption Standards) and SSL (Secure Socket Layer) protocols. Your payment details are 100% secure as we do not share your payment details with third party sellers.

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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