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The Hundred-Page Machine Learning Book
90% of respondents would recommend this to a friend
HNL 1953
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The Hundred-Page Machine Learning Book is a comprehensive, yet concise, guide to machine learning that covers all major approaches and is accessible to almost anyone
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What Stands Out
Detalles de producto
- Written by Peter Norvig, Research Director at Google
- Covers both theory and practice of machine learning
- Provides a solid introduction to the field in just 100 pages
- Includes math equations and core concepts explained concisely
- Useful for newcomers and experienced practitioners
- Covers major machine learning approaches and includes Python code examples
| Publisher | Andriy Burkov |
| Publication date | 13 Jan. 2019 |
| Language | English |
| Print length | 160 pages |
| ISBN-10 | 199957950X |
| ISBN-13 | 978-1999579500 |
| Item weight | 376 g |
| Dimensions | 19.05 x 0.97 x 23.5 cm |
| Part of series | The Hundred-Page Books |
Who Should Buy?
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Beginners in ML
Ideal for novices seeking a concise introduction to machine learning concepts and algorithms without overwhelming details.
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Busy Professionals
Great for those needing a quick yet comprehensive overview of machine learning principles to enhance their skills efficiently.
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Students of Data Science
Perfect for students looking for a clear, structured resource to supplement their courses and deepen their understanding.
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Advanced Practitioners
Not suitable for experienced machine learning professionals seeking in-depth technical discussions or cutting-edge research findings.
DESCRIPCIÓN DEL PRODUCTO
The Hundred-Page Machine Learning Book
About This Item
The Hundred-Page Machine Learning Book Are you looking to delve into the world of machine learning but don't know where to start? Look no further than The Hundred-Page Machine Learning Book. This comprehensive guide will take you on an exciting journey, from the basics to more advanced concepts, all condensed into a concise hundred-page read. Whether you're a beginner or have some prior knowledge in the field, this book is designed to provide a solid foundation in machine learning. With clear explanations and practical examples, it breaks down complex topics into easily understandable bites, allowing you to grasp the core concepts with ease. Written by a team of industry experts, this book covers a wide range of machine learning techniques and algorithms, including supervised and unsupervised learning, decision trees, neural networks, and deep learning.
You will also learn about model selection, evaluation, and deployment strategies to make the most out of your machine learning projects. Not only is The Hundred-Page Machine Learning Book informative, but it is also highly accessible. Its concise format ensures that you won't get lost in lengthy explanations or overly technical jargon. Instead, you'll be able to quickly grasp the essentials and start applying your newfound knowledge in real-world scenarios. Whether you're a student, a professional looking to upskill, or simply curious about the fascinating world of machine learning, this book is the perfect companion.
With its practical approach, you'll be on your way to becoming a proficient machine learning practitioner in no time. Don't miss out on this must-have resource for anyone interested in machine learning. Get your copy of The Hundred-Page Machine Learning Book today and unlock the potential of this rapidly advancing field.
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AI & Machine Learning Editorial Review
The Hundred-Page Machine Learning Book is authored by Andriy Burkov and was published on January 13, 2019. This 160-page resource is crafted for those with a mathematical background who are looking to delve into machine learning. Reviewers appreciate its rigorous yet concise explanations and the way it clarifies complex notations and concepts. The book covers a range of topics from fundamental principles to practical algorithms, making it a vital tool for understanding machine learning theory and its applications. However, readers note that it may not be ideal for absolute beginners, as a foundational knowledge of machine learning would be beneficial.
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ventajas
- Concise explanations of complex concepts
- Covers practical ML problems effectively
- Clarifies mathematical notations well
- Suitable for readers with some ML background
- Offers insights on unique ML techniques
Contras
- Content may be too dense for absolute beginners
Product Price History
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HNL 1953
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características y beneficios
- Covers all major machine learning approaches
- Accessible to almost anyone without high level mathematical knowledge or programming experience
- Provides a solid introduction to the field for newcomers, but also useful for experienced professionals
- Includes details on various approaches and innovative companion book wiki
- Short enough to read in a single sitting but comprehensive enough to serve as a reference
- Ideal for anyone starting a PhD program
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