Machine Learning Fundamentals: Use Python and scikit-learn to get up and running with the hottest developments in machine learning
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Machine Learning Fundamentals: Use Python and scikit-learn to get up and running with the hottest developments in machine learning
Build machine learning algorithms that optimize the programming process and take application performance to a whole new level
Machine Learning Fundamentals: Use Python and scikit-learn to get up and running with the hottest developments in machine learning
Numéro d'article: 41075554

Machine Learning Fundamentals: Use Python and scikit-learn to get up and running with the hottest developments in machine learning

89% des répondants recommanderaient ceci à un ami

Numéro d'article: 41075554

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Ce qui se démarque

Comprehensive Curriculum
Offers a thorough exploration of machine learning principles, empowering learners to master Python and scikit-learn effectively while addressing common challenges faced by aspiring data scientists.
Hands-On Projects
Incorporates practical, real-world projects that reinforce learning, allowing users to build a strong portfolio and apply algorithms effectively to solve complex data problems.
Latest Advancements
Stays current with the most recent trends and techniques in machine learning, equipping users with cutting-edge knowledge and tools to excel in a competitive landscape.

Détails du produit

Learn the fundamentals of Machine Learning and stay updated with the latest developments using Python and scikit-learn. Join Ubuy Republic of the Congo for the best deals on machine learning resources.
  • Explore scikit-learn uniform API and its application into any type of model
  • Understand the difference between supervised and unsupervised models
  • Learn the usage of machine learning through real-world examples
  • Gain insights into the differences between supervised and unsupervised models
  • Implement a confusion matrix using scikit-learn
  • Designed for developers new to machine learning with knowledge of Python programming
Publisher Packt Publishing
Publication date November 29, 2018
Language English
Print length 240 pages
ISBN-10 1789803551
ISBN-13 978-1789803556
Item Weight 7.4 ounces (209.79 grams)
Dimensions 7.5 x 0.55 x 9.25 inches (19.1 x 1.4 x 23.5 cm)

À qui est-ce destiné ?

Suitable For
  • Aspiring Data Scientists

    Ideal for those seeking to enter the field, offering practical knowledge of machine learning algorithms using Python.

  • Software Developers

    Recommended for developers looking to enhance their skills and integrate machine learning capabilities into their applications.

  • Students in AI

    Great resource for students wanting to understand machine learning concepts and apply them through hands-on projects.

Not Suitable For
  • Beginner Programmers

    Not suitable for those without prior programming experience, as it assumes a basic understanding of Python.

DESCRIPTION DU PRODUIT

Machine Learning Fundamentals: Use Python and scikit-learn to get up and running with the hottest developments in machine learning

Vous avez une question ? Chattez avec nous

Questions et réponses des clients

  • question: What is 'Machine Learning Fundamentals: Use Python and scikit-learn' about?

    répondre: This book serves as an introductory guide to machine learning using Python and the scikit-learn framework. It covers essential theories, algorithms, and practical applications of machine learning, enabling readers to grasp fundamental concepts effectively. Through hands-on examples and projects, readers will learn how to implement machine learning models, enhancing their programming skills and understanding of data science. This foundational knowledge is crucial for those looking to delve deeper into AI and data analysis.
  • question: Who is the target audience for this book?

    répondre: The target audience for 'Machine Learning Fundamentals' primarily includes beginners in data science and programming enthusiasts eager to grasp machine learning concepts. It’s ideal for students, professionals, or hobbyists with a basic understanding of Python. The book is designed to cater to various learning styles, making it suitable for self-learners and those in academic settings seeking to enhance their machine learning capabilities.
  • question: Do I need prior programming experience to understand this book?

    répondre: While it is beneficial to have some basic programming knowledge, especially in Python, it is not strictly necessary to start learning from this book. It introduces machine learning concepts progressively and often explains fundamental programming principles as they relate to the material. Beginners with a willingness to learn will find the explanations thorough enough to help them gain the required skills along the way.
  • question: How does 'Machine Learning Fundamentals' differ from other machine learning books?

    répondre: Unlike many machine learning books that delve into complex theories, 'Machine Learning Fundamentals' focuses on practical implementation using Python and scikit-learn. It offers step-by-step guidance, real-world examples, and clear explanations, making it more accessible for newcomers. This hands-on approach helps readers not only learn theoretical aspects but also apply them in practical scenarios, reinforcing their understanding.
  • question: What topics are covered in the book?

    répondre: The book covers a broad range of topics, including supervised and unsupervised learning, data preprocessing, feature selection, model evaluation, and more. Additionally, readers will explore popular algorithms like linear regression, decision trees, and clustering methods. Each section includes practical examples and projects, ensuring that readers not only understand the concepts but can also apply them effectively in real-world situations.
  • question: Can this book help me with machine learning projects in my job?

    répondre: Yes, 'Machine Learning Fundamentals' can significantly enhance your ability to tackle machine learning projects at work. By providing a solid foundation in various machine learning techniques and tools, it equips you with the skills to analyze datasets, build predictive models, and derive insights relevant to your industry. Practical case studies in the book can also inspire application ideas tailored to your job requirements.
  • question: Is this book suitable for experienced programmers looking to learn machine learning?

    répondre: Even though the book is beginner-friendly, experienced programmers can also benefit from it by gaining insights into machine learning best practices and the intricacies of scikit-learn. The practical exercises provided can serve as a valuable resource for reinforcing knowledge through application. Moreover, seasoned developers can quickly familiarize themselves with machine learning concepts, enhancing their skill set for data-driven projects.
  • question: Where can I find examples and exercises to practice machine learning?

    répondre: In 'Machine Learning Fundamentals', numerous examples and exercises are provided to reinforce learning. Each chapter typically concludes with hands-on exercises that encourage readers to apply what they’ve learned. Additionally, the book may reference online resources and communities where readers can find further practice materials and real-world datasets, fostering a deeper understanding of machine learning applications.
  • question: How can I stay updated on developments in machine learning after reading this book?

    répondre: To stay updated on machine learning developments, consider following blogs, research journals, and online courses related to data science and machine learning. Engaging in communities such as GitHub or data science forums can also provide insights into new libraries and techniques. Attending webinars or workshops focused on emerging trends in machine learning will allow you to further expand your knowledge and skills post-reading.
  • question: Where can I buy 'Machine Learning Fundamentals' in Republic of the Congo?

    répondre: You can buy 'Machine Learning Fundamentals: Use Python and scikit-learn' on Ubuy, a reliable online shopping platform that provides a wide range of books including educational and technical titles. Ubuy ensures a seamless shopping experience and reliable delivery options tailored for customers in Republic of the Congo, making it easy for you to access this valuable resource on machine learning.

Intelligence & Semantics Editorial Review

**** Customers have responded enthusiastically to this machine learning book, praising it for its clarity and practical approach to a complex subject. Many readers noted that the book serves as a guided introduction, particularly for beginners, effectively demystifying terms and concepts that often cause confusion. Reviewers specifically highlighted the thorough explanations provided for basic terminologies and processes, such as handling messy data, NaN values, and normalization techniques. The author employs a hands-on method, incorporating immediate exercises that reinforce learning, which has been appreciated by users transitioning into real-world applications of machine learning. Compared to other resources, this book stands out as a comprehensive starting point, making complex theories accessible to those unfamiliar with the field. Users have expressed their satisfaction with the structure of the book; the blend of theory and practical application has made it an effective educational tool. Overall, the author’s simplicity in language and clarity has resonated well with readers, leading to strong recommendations for anyone embarking on their machine learning journey. **

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Avantages

  • Clear and accessible explanations of complex topics
  • Immediate exercises for practical learning
  • Comprehensive coverage of essential machine learning concepts
  • Real-world applications discussed alongside theory
  • Ideal for beginners in machine learning

Les inconvénients

  • Some readers felt overwhelmed by the content early on

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Caractéristiques et avantages

  • Explore scikit-learn uniform API and its application into any type of model
  • Understand the difference between supervised and unsupervised models
  • Learn the usage of machine learning through real-world examples
  • Gain insights into the differences between supervised and unsupervised models
  • Implement different supervised algorithms and develop neural network structures using the scikit-learn package
  • Learn to build a comprehensive machine learning program

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