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Item #:
3695470
Real-World Machine Learning
88% of respondents would recommend this to a friend
Item #:
3695470
€ 54
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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 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.
Product Details
- 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 |
| 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.
Product Description
Real-World Machine Learning
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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Product Buying Guide
Real-World Machine Learning First Edition is a practical guide designed for working developers interested in mastering the art of ML project execution without delving into academic theory and complex mathematics. It introduces the day-to-day practice of machine learning, preparing readers to successfully build and deploy powerful ML systems. The book is suitable for readers who have no prior machine learning experience but are familiar with Python.
Product Specifications
- Title: Real-World Machine Learning First Edition
- Authors: Henrik Brink, Joseph Richards, Mark Fetherolf
- Format: Printed book with free eBook in PDF, Kindle, and ePub formats from Manning Publications
- Language: English
- Pages: Not specified
Key Features
- Teaches practical concepts and techniques of machine learning
- Includes immediately relevant examples in Python for hands-on learning
- Covers data acquisition, modeling, classification, regression, model validation, optimization, and scalability
- Suitable for readers with no prior machine learning experience but familiarity with Python
Usage Scenarios
- Predicting future behavior
- Performance evaluation and optimization
- Analyzing sentiment and making recommendations
- Building, deploying, and maintaining powerful ML systems
Usage Scenarios
Competitors can include books or online courses focused on practical machine learning skills development, such as 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems' by Aurélien Géron or 'Python Machine Learning' by Sebastian Raschka.
Some User Review
- Readers praise the practical approach to machine learning concepts
- The use of Python examples makes it easier to understand and implement the concepts taught
- Appreciation for focusing on real-world applications and not overwhelming readers with complex mathematics
Competitors
- The price range for similar books and online courses on practical machine learning skills development can vary. It is advisable to compare prices from different vendors and consider bundled offers that include additional resources or formats.
- Consider the different available formats (e.g., printed book, eBook) and their respective prices to determine the most cost-effective option based on individual preferences.
Buying Considerations
- Consider assessing personal learning style and the preferred format for learning materials (printed book, eBook) to ensure an optimal learning experience
- Check for any bundled offers or additional resources included with the purchase, such as companion code repositories, online forums, or supplementary materials, which can add value to the learning process
Conclusion
Real-World Machine Learning First Edition is a valuable resource for working developers aiming to master the practical execution of machine learning projects. With a focus on real-world applications and hands-on examples in Python, it equips readers with essential skills to build, deploy, and maintain powerful ML systems.
View LessReal-World Machine Learning First Edition is a practical guide designed for working developers interested in mastering the art of ML project execution without delving into academic theory and complex mathematics. It introduces the day-to-day practice of machine learning, preparing readers to successfully build and deploy powerful ML systems. The book is suitable for readers who have no prior machine learning experience but are familiar with Python. Continue Reading
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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.
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Pros
- 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
Cons
- Not an introductory book
Product Price History
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€ 54
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Features & Benefits
- 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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