Kernel Methods for Machine Learning with Math and Python: 100 Exercises for Building Logic, (Paperback)
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The most crucial ability for machine learning and data science is mathematical logic for grasping their essence.
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Detalji o proizvodu
| Book format | Paperback |
| Fiction/nonfiction | Non-Fiction |
| Genre | Computing & Internet |
| Publication date | May, 2022 |
| Pages | 208 |
| Subgenre | Artificial Intelligence |
| Series title | No Series |
| Edition | 2022 Edition |
| Publisher | Springer Nature Singapore |
| Original languages | English |
| Language | English |
| Edu focus | Mathematics |
| Educational level | General |
| Is collectible | N |
| Recording time | 0 min |
| Retail packaging | Single Piece |
| Assembled product dimensions (l x w x h) | 6.14 x 0.47 x 9.21 in (15.6 x 1.2 x 23.4 cm) |
| Assembled product weight | 0.7 lb (320 grams) |
| Bisac subject heading | Computers |
Who Should Buy?
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Students in ML
Ideal for students pursuing machine learning, providing practical exercises to solidify theoretical concepts through Python coding.
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Data Scientists
Useful for data scientists looking to enhance their understanding of kernel methods and their applications in real projects.
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Self-learners
Great resource for self-learners who want to practice machine learning at their own pace with guided exercises.
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Beginners in Coding
Not suitable for absolute beginners in programming, as prior knowledge of Python and machine learning is required.
OPIS PROIZVODA
Kernel Methods for Machine Learning with Math and Python: 100 Exercises for Building Logic, (Paperback)
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Joe Suzuki All Books Editorial Review
Kernel Methods for Machine Learning with Math and Python: 100 Exercises for Building Logic offers a comprehensive guide for learners interested in artificial intelligence and machine learning. This paperback book, published in May 2022, spans 208 pages, focusing on mathematics and its application in the field. It provides 100 practical exercises designed to build logic in machine learning, making it suitable for general educational levels. Readers appreciate the clarity and practical approach of the content, affirming that it simplifies complex concepts for better understanding. This resource is especially beneficial for those seeking a non-fiction read that enhances their knowledge of computing and internet technologies.
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Pros
- Includes 100 practical exercises for hands-on learning
- Focuses on mathematics in machine learning
- Suitable for general educational levels
- Clear and accessible explanations provided
- Published by a reputable publisher, Springer
Protiv
- No collectible features noted for book enthusiasts
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Značajke i prednosti
- Learn the fundamentals of kernel methods for machine learning.
- Includes 100 carefully selected exercises with solutions.
- Written in an easy-to-follow and self-contained style.
- No prior knowledge of mathematics is assumed; suitable for beginners.
- Covers distinct types of kernels, including RKHS and Gaussian processes.
- Enhance your programming skills with source programs and running examples.
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