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43340608
Machine Learning: An Applied Mathematics Introduction, (Paperback)
94% of respondents would recommend this to a friend
Item #:
43340608
MYR 151
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Machine Learning: An Applied Mathematics Introduction covers the essential mathematics behind all of the following topics
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What Stands Out
Applied Focus
This book emphasizes practical applications of machine learning concepts, making it ideal for those looking to implement theory in real-world scenarios.
Mathematical Foundation
It uniquely combines machine learning with applied mathematics, ensuring readers build a strong theoretical understanding to enhance their analytical skills in various fields.
Comprehensive Coverage
Offers extensive coverage of key algorithms and techniques, enabling a thorough grasp of the subject that caters to both beginners and advanced learners.
Maklumat produk
- Covers essential mathematics behind topics like K Nearest Neighbours, K Means Clustering, Naïve Bayes Classifier, and more
- Includes real-world examples from fields such as finance, economics, politics, biology, sociology, gambling, and business
- Authored by Paul Wilmott, who brings three decades of experience in mathematics education
- Designed as an accessible introduction for those seeking to understand the foundations of machine learning
- Offers a practical approach for readers who want to grasp the core concepts efficiently
- Presents an inimitable style of teaching to make the subject engaging and comprehensible
| Book format | Paperback |
| Fiction/nonfiction | Non-Fiction |
| Genre | Textbooks |
| Publication date | May, 2019 |
| Pages | 242 |
| Subgenre | General |
| Series title | No Series |
| Edition | 1 |
| Publisher | Panda Ohana Publishing |
| Original languages | English |
| Language | English |
| Edu focus | Math & Counting |
| Educational level | General |
| Is collectible | N |
| Binding type | Perfect Binding |
| Recording time | 0 min |
| Retail packaging | Single Piece |
| Assembled product dimensions (l x w x h) | 6.14 x 0.51 x 9.21 in (15.6 x 1.3 x 23.4 cm) |
| Assembled product weight | 0.76 lb (340 grams) |
| Bisac subject heading | Mathematics |
Who Should Buy?
-
Mathematics Students
Ideal for those studying applied mathematics, providing a solid foundation for understanding machine learning concepts.
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Data Science Beginners
Perfect for newcomers to data science who need to grasp mathematical principles underpinning machine learning techniques.
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Professionals in Tech
Beneficial for tech professionals seeking to enhance their skills in machine learning through an applied mathematics lens.
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Complete Beginners
Not suitable for individuals with no prior knowledge of mathematics or machine learning concepts.
PENERANGAN PRODUK
Machine Learning: An Applied Mathematics Introduction, (Paperback)
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Paul Wilmott All Books Editorial Review
Machine Learning: An Applied Mathematics Introduction (Paperback) is a comprehensive guide that delves into the mathematical foundations crucial for understanding machine learning concepts. This non-fiction textbook, published in May 2019, spans 242 pages, making it an excellent resource for learners at a general educational level. With a perfect binding format, it's designed for easy handling during study sessions. The book adeptly bridges the gap between mathematics and its application in machine learning, marking it as an essential read. Readers can expect a thorough exploration of math and counting essential for grasping machine learning algorithms and techniques.
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Ulas Produk Ini
Kongsikan pandangan anda dengan pelanggan lain
Kebaikan
- Accessible for general educational levels
- Covers essential mathematical concepts
- Well-structured for easy comprehension
- Perfect binding ensures durability
- Lightweight and portable format
Keburukan
- May lack advanced topics for experienced learners
Product Price History
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MYR 151
Pesan sekarang dan dapatkannya lebih kurang pada Rabu, September 23
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- K Nearest Neighbours
- K Means Clustering
- Naive Bayes Classifier
- Regression Methods
- Support Vector Machines
- Self-Organizing Maps
- Decision Trees
- Neural Networks
- Reinforcement Learning
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