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Hands-On Gradient Boosting with XGBoost and scikit-learn: Perform accessible machine learning and extreme gradient boosting with Python
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Hands-On Gradient Boosting with XGBoost and scikit-learn: Perform accessible machine learning and extreme gradient boosting with Python
By the end of the book, you'll be able to build high-performing machine learning models using XGBoost with minimal errors and maximum speed.
Hands-On Gradient Boosting with XGBoost and scikit-learn: Perform accessible machine learning and extreme gradient boosting with Python
Artigo n.º: 38467082

Hands-On Gradient Boosting with XGBoost and scikit-learn: Perform accessible machine learning and extreme gradient boosting with Python

Artigo n.º: 38467082

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By the end of the book, you'll be able to build high-performing machine learning models using XGBoost with minimal errors and maximum speed.
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O que se Destaca

Expert Guidance
Learn from industry professionals who provide in-depth explanations and practical exercises, ensuring a strong understanding of machine learning concepts and applications.
Comprehensive Curriculum
Cover both XGBoost and scikit-learn thoroughly, equipping learners with versatile tools needed to tackle diverse machine learning tasks effectively.
Hands-On Experience
Engage in practical, real-world projects that enhance skill application, making it easier to translate theoretical knowledge into actionable insights.

Detalhes do produto

Discover how to perform machine learning and extreme gradient boosting with Python. Get hands-on experience with XGBoost and scikit-learn. Shop now at Ubuy Cape Verde.
Item Weight1 lbs (450 grams)

Quem Deverá Comprar?

Suitable For
  • Aspiring Data Scientists

    Ideal for beginners aiming to learn gradient boosting techniques and enhance their skills in machine learning applications.

  • Professionals in Analytics

    Beneficial for analysts seeking to improve predictive model performance using advanced methods like XGBoost and scikit-learn.

  • Machine Learning Instructors

    Useful for educators teaching machine learning concepts, providing practical insights into implementing gradient boosting models.

Not Suitable For
  • Absolute Beginners

    Not suitable for those with no prior programming or data science experience, as it requires fundamental knowledge.

  • Casual Learners

    May not engage users looking for light reading or non-technical discussions rather than in-depth practical applications.

  • Non-Technical Users

    Does not cater to users with no technical background who might struggle with coding and mathematical concepts.

DESCRIÇÃO DO PRODUTO

Hands-On Gradient Boosting with XGBoost and scikit-learn: Perform accessible machine learning and extreme gradient boosting with Python

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Perguntas e Respostas dos Clientes

  • pergunta: What is the primary focus of 'Hands-On Gradient Boosting with XGBoost and scikit-learn'?

    Resposta: The book focuses on providing practical insights into machine learning techniques using Python, particularly emphasizing gradient boosting methods like XGBoost and the scikit-learn library. It blends theoretical concepts with hands-on coding to empower readers to implement these advanced algorithms effectively. Users can expect to learn about real-world applications, such as predictive analytics and data classification, making it highly relevant for data scientists and machine learning enthusiasts looking to enhance their skills.
  • pergunta: Is prior experience in machine learning necessary to use this book?

    Resposta: While the book is designed to be accessible to beginners, a basic understanding of Python and machine learning concepts will greatly enhance your learning experience. It introduces fundamental principles and gradually builds up to more complex topics. Users new to machine learning can benefit from the step-by-step instructions and clear examples. For those with more experience, it offers deeper insights into implementing and optimizing gradient boosting techniques.
  • pergunta: How does this book differ from other machine learning resources?

    Resposta: This book stands out by focusing specifically on gradient boosting and its practical implementation through XGBoost and scikit-learn. Unlike many resources that cover a broad array of topics, it delves deeply into the intricacies of boosting algorithms, providing detailed coding examples and relevant use cases. It is particularly beneficial for readers looking to specialize in ensemble methods and performance tuning, ensuring they are well-prepared to tackle real-world data challenges.
  • pergunta: Can this book help with real-time data applications?

    Resposta: Absolutely, the book is structured to address real-time data processing and analysis scenarios. By utilizing XGBoost and scikit-learn, readers will learn how to build models that can handle live data inputs effectively. Practical examples included in the text illustrate applications in areas such as fraud detection, stock price prediction, and dynamic customer segmentation, equipping readers with the tools needed for immediate application in various industries.
  • pergunta: What level of Python proficiency is assumed for readers of this book?

    Resposta: The book assumes a foundational knowledge of Python, including basic syntax, data structures, and libraries. It is designed to guide readers through the more advanced Python concepts required for implementing machine learning algorithms. Users comfortable with programming in Python will find the transition smoother, while those with only a basic understanding can still follow along with practice and dedication. The hands-on approach also encourages learning through coding directly.
  • pergunta: Are there any prerequisites for learning gradient boosting in this book?

    Resposta: While there are no strict prerequisites, familiarity with machine learning concepts and experience with Python will greatly benefit readers. Basic knowledge of statistics and linear algebra can also enhance comprehension of advanced topics. The book progressively introduces concepts, but having a grounding in these areas will enable readers to grasp the intricacies of gradient boosting techniques more effectively, making their learning experience more enriching.
  • pergunta: How can I apply what I learn from this book to a job in data science?

    Resposta: The skills gained from this book are directly applicable to roles in data science and analytics. By mastering gradient boosting and the practical applications discussed, readers can enhance their resume and portfolio with relevant projects. Additionally, understanding these advanced algorithms will give an edge in job interviews, as many companies look for candidates proficient in machine learning. Use case examples provided can also serve as practical references during interviews, showcasing real-world problem-solving skills.
  • pergunta: What machine learning problems can be solved using XGBoost as described in the book?

    Resposta: XGBoost is a powerful algorithm that can tackle a variety of machine learning problems, including classification, regression, and ranking tasks. The book presents case studies and examples that cover real-world applications such as customer churn prediction, credit scoring, and image classification. By implementing these techniques, readers will understand how to derive valuable insights from complex datasets and achieve better performance over traditional methods.
  • pergunta: Does the book provide code samples for practice?

    Resposta: Yes, the book is rich with code samples and exercises designed for hands-on practice. Each chapter includes practical coding examples that illustrate the application of gradient boosting techniques using XGBoost and scikit-learn. Readers are encouraged to run these examples themselves, modifying the code to gain a deeper understanding of the concepts. This hands-on approach solidifies knowledge as readers apply theoretical principles to real-world scenarios.
  • pergunta: Where can I buy 'Hands-On Gradient Boosting with XGBoost and scikit-learn'?

    Resposta: You can purchase 'Hands-On Gradient Boosting with XGBoost and scikit-learn' from Ubuy in Cape Verde. Ubuy offers a wide selection of books and resources that cater to your learning needs in data science and machine learning. With Ubuy, you can easily find this title along with related materials around machine learning and Python programming to enhance your skills.

Neural Networks Editorial Review

"Hands-On Gradient Boosting with XGBoost and scikit-learn" is a book that offers an excellent overview of XGBoost and tree/ensemble methods in general. The author presents the material in a clear and concise way, peeling back the layers of the algorithms while exposing their advantages and shortcomings. The book covers in detail all hyperparameters and how to tune them systematically. The 'Kaggle Masters' section offers a great way to challenge experienced users. The author shows a 'full ML pipeline' with preprocessing, models, etc. The only drawback is that the book fails to show how to handle preprocessing predict files when the transformers need to be saved, but this is a small issue compared to the wealth of information and knowledge the book offers.

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Prós

  • Excellent overview of XGBoost and tree/ensemble methods
  • Clear and concise presentation of material
  • Detailed coverage of hyperparameters and tuning
  • Offers a 'Kaggle Masters' section for experienced users
  • Covers the full ML pipeline with preprocessing, models, etc.

Contras

  • Does not show how to handle preprocessing predict files when transformers need to be saved

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CVE 6508

Encomende já e receba por volta de Quinta-feira, Julho 23

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Recursos e benefícios

  • Learn to build gradient boosting models from scratch
  • Develop XGBoost regressors and classifiers with accuracy and speed
  • Customize transformers and pipelines to deploy XGBoost models
  • Discover tips and tricks from XGBoost Kaggle winners
  • Apply alternative base learners like dart, linear models, and XGBoost random forests
  • Build high-performing machine learning models using XGBoost with minimal errors and maximum speed

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