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Nº de artículo:
3695470
Real-World Machine Learning
88% of respondents would recommend this to a friend
Nº de artículo:
3695470
PEN 236
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What Stands Out
Practical Applications
Focuses on real-world applications of machine learning, providing hands-on examples that enhance understanding and retention, making it ideal for practitioners and students aiming to implement ML solutions effectively.
Comprehensive Coverage
Covers a wide range of machine learning techniques and algorithms, ensuring readers gain a thorough understanding of both foundational concepts and advanced topics, leading to well-rounded expertise in the field.
Accessible Learning
Designed for varied skill levels, the book employs clear explanations and practical exercises, making machine learning accessible to beginners while still offering valuable insights for more experienced learners.
Detalles de producto
| Publisher | Manning |
| Publication date | September 30, 2016 |
| Edition | First Edition |
| Language | English |
| Print length | 264 pages |
| ISBN-10 | 9781617291920 |
| ISBN-13 | 978-1617291920 |
| Item Weight | 1 pounds (450 grams) |
| Dimensions | 7.38 x 0.5 x 9.25 inches (18.7 x 1.3 x 23.5 cm) |
Who Should Buy?
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Aspiring Data Scientists
Provides practical insights and hands-on examples to help beginners start their journey in machine learning.
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Industry Professionals
Useful for professionals looking to apply machine learning techniques in real-world applications across various industries.
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Academic Learners
Designed for students seeking to understand the practicalities of machine learning beyond theoretical foundations.
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Complete Beginners
Those with no prior knowledge of programming or statistics may find it challenging to grasp the content.
DESCRIPCIÓN DEL PRODUCTO
Real-World Machine Learning
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Software Development Editorial Review
Real-World Machine Learning is a well-organized and presented book that is useful for anyone learning about machine learning, providing a cohesive approach for explaining and illustrating the practical steps involved in building and evaluating models. Although some reviewers warn it is not an introductory book and is too skimpy on mathematical details, it provides great project ideas and code examples for feature engineering and scaling of machine learning systems. The book is aimed at intermediate-level developers with prior basic familiarity with Python coding and algorithms, making it a useful companion for programmers wanting to adopt machine learning practices. The strengths of the book are in its coverage of feature engineering and scaling of machine learning systems. One weakness highlighted is its presentation of figures in black-and-white, making them less useful, but the color figures are available in the easy-to-access PDF version. Overall, it is a highly readable, practical book that addresses all real-life issues in machine learning.
Customer Reviews & Ratings
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ventajas
- Well-organized and presented
- Good systematic approach for explaining and illustrating practical steps
- Good project ideas and code examples for feature engineering and scaling of machine learning systems
- Useful for anyone learning about machine learning
- No mathematical background required
- No prior familiarity with various types of statistical models used in ML necessary
- Strengths in coverage of feature engineering and scaling of machine learning systems
- Authors demonstrate hands-on knowledge of the subject
- Example Notebooks and associated data available for download
Contras
- Not an introductory book
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