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Mahsulot #:
108605568
Mastering Machine Learning, (Paperback)
Mahsulot #:
108605568
UZS 624331
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Mahsulot tafsilotlari
| Book format | Paperback |
| Fiction/nonfiction | Non-Fiction |
| Genre | Computing & Internet |
| Publication date | March, 2024 |
| Pages | 306 |
| Subgenre | Machine Theory |
| Series title | No Series |
| Edition | 1 |
| Publisher | Eldonusa |
| Original languages | English |
| Language | English |
| Edu focus | Technology |
| Is collectible | N |
| Recording time | 0 min |
| Retail packaging | Single Piece |
| Assembled product dimensions (l x w x h) | 6.00 x 0.64 x 9.00 in (15.2 x 1.6 x 22.9 cm) |
| Assembled product weight | 0.91 lb (410 grams) |
| Bisac subject heading | Computers |
MAHSULOT TAVSIFI
Mastering Machine Learning, (Paperback)
Savolingiz bormi? Biz bilan suhbatlashish
Dr Hesham Mohamed Elsherif All Books Editorial Review
Mastering Machine Learning, a non-fiction paperback published by Eldonusa, delves into the intricacies of machine theory within the realm of computing and internet technology. With a substantial 306 pages, this book aims to provide a comprehensive understanding of machine learning concepts and applications. It marks its first edition and is expected to be a valuable resource for technology-focused readers just as it launches in March 2024. Readers can anticipate a thorough exploration of machine learning mechanics, making it a relevant addition to their educational toolkit.
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Pros
- Comprehensive coverage of machine learning topics
- Well-structured for ease of understanding
- Published by a reputable company
- Suitable for technology enthusiasts
- Thorough explanations for complex concepts
Kamchiliklari
- May not include the latest advancements post-publication
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UZS 624331
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Xususiyatlar va Afzalliklar
- Comprehensive guide for all levels, from newcomers to seasoned programmers.
- Covers fundamental programming languages and deep learning concepts.
- Hands-on approach with practical examples and real-world applications.
- Emphasizes understanding and evaluating machine learning models.
- Addresses ethical implications, ensuring responsible usage.
- Essential for students, researchers, data scientists, and AI enthusiasts.
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