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Nº de artículo:
195274761
Real-World Machine Learning Paperback – Illustrated, 6 Oct. 2016
88% of respondents would recommend this to a friend
Nº de artículo:
195274761
USD 36
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Real-World Machine Learning is a practical guide designed to teach working developers the art of ML project execution.
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What Stands Out
Practical Insights
Offers real-world applications and practical knowledge, allowing readers to implement machine learning techniques effectively in various scenarios.
Illustrated Content
Features clear illustrations that simplify complex concepts, making it accessible for learners at different levels of expertise.
Updated Techniques
Covers the latest advancements in machine learning, ensuring readers stay current with the evolving landscape of technology and methodologies.
Detalles de producto
- Practical guide for working developers to execute ML projects without overwhelming academic theory and complex mathematics
- Introduces day-to-day practice of machine learning to successfully build and deploy powerful ML systems
- Purchase includes free eBook in PDF, Kindle, and ePub formats from Manning Publications
- Teaches concepts and techniques needed without overdosing on abstract theory and complex mathematics
- Covers immediately relevant examples in Python, including data acquisition, modeling, classification, and regression
- Helps readers develop skills in model validation, optimization, scalability, and real-time streaming
| Publisher | Manning Publications |
| Publication date | 6 Oct. 2016 |
| Edition | 1st |
| Language | English |
| Print length | 264 pages |
| ISBN-10 | 9781617291920 |
| ISBN-13 | 978-1617291920 |
| Item weight | 454 g |
| Dimensions | 18.75 x 1.27 x 23.5 cm |
Who Should Buy?
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Data Science Students
Ideal for students pursuing data science who need practical insights and real-world applications of machine learning techniques.
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Industry Professionals
Beneficial for professionals seeking to enhance their knowledge of machine learning applications in various industries.
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Self-Learners
Great for self-taught individuals looking for clear, actionable guidance on machine learning principles and practices.
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Beginner Programmers
Not suitable for those with no coding or statistical background as it requires a foundational understanding of programming.
DESCRIPCIÓN DEL PRODUCTO
Real-World Machine Learning Paperback – Illustrated, 6 Oct. 2016
About This Item
Introducing the Real-World Machine Learning First Edition Book Are you ready to take your machine learning skills to the next level and apply them to real-world applications? Look no further than the Real-World Machine Learning First Edition book. Whether you're a beginner or an experienced practitioner, this book will provide you with the knowledge and techniques needed to implement machine learning in practical scenarios. Inside this book, you'll find a comprehensive guide filled with real-life examples and case studies showcasing the power of machine learning in various industries. From finance to healthcare, marketing to transportation, this book covers it all. Learn how to analyze real-world data sets and uncover valuable insights using machine learning algorithms.
Discover best practices for implementing machine learning models and techniques for overcoming common challenges. Gain a deep understanding of the latest advancements and strategies in the field of machine learning. With the Real-World Machine Learning First Edition book, you'll have everything you need to excel in the world of machine learning. Whether you're a student, a data scientist, or a business professional, this book is a must-have resource for anyone looking to harness the power of machine learning in their daily work. Don't miss out on this opportunity to expand your machine learning skills and stay ahead of the competition.
Order your copy of the Real-World Machine Learning First Edition book today and unlock the potential of machine learning in the real world.
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Data Mining Editorial Review
** Real-World Machine Learning** "Real-World Machine Learning" has garnered a positive reception from readers who appreciate its practical approach to the subject. The book is celebrated for its clarity and accessibility, making complex concepts in machine learning understandable for a broad audience, including beginners and those with some prior knowledge. Customers highlight the well-structured content that provides a strong foundation in both theoretical and practical aspects of machine learning. Several readers commend the author's engaging writing style, which is noted for being both informative and easy to follow. The inclusion of real-world examples and case studies facilitates a deeper understanding of how machine learning can be applied in everyday scenarios. Readers also appreciate the comprehensive coverage of various algorithms and techniques, which are discussed in a context that emphasizes their practical implications. Furthermore, the book is often praised for its balance between theory and application, making it a valuable resource not only for individuals seeking to learn machine learning but also for professionals looking to implement these techniques in real-world projects. Some customers have also drawn attention to the helpful exercises at the end of each chapter, which reinforce learning and allow readers to test their knowledge practically. Overall, "Real-World Machine Learning" is positioned as a highly recommended resource for those looking to enhance their understanding of machine learning and its applications, appealing to both novices and more experienced readers. **
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ventajas
- Clear and accessible writing style
- Well-structured content with practical applications
- Engaging real-world examples and case studies
- Comprehensive coverage of algorithms and techniques
- Helpful exercises for reinforcing learning
Contras
- May require some prior knowledge for complete comprehension
Product Price History
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USD 36
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características y beneficios
- The book introduces the day-to-day practice of machine learning.
- It focuses on preparing developers to successfully build and deploy powerful ML systems.
- The book includes immediately relevant examples in Python, as well as tasks like model validation, optimization, scalability, and real-time streaming.
- No prior machine learning experience is assumed, but readers should know Python.
- The authors are experienced data scientists engaged in the daily practice of machine learning.
- The book is divided into two parts, the machine-learning workflow, and practical application.
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