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Item #:
43340608
Machine Learning: An Applied Mathematics Introduction, (Paperback)
94% of respondents would recommend this to a friend
Item #:
43340608
TTD 212
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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.
Product Details
- Covers essential mathematics behind topics like K Nearest Neighbours, Regression Methods, and Neural Networks
- Includes real-world examples from various fields such as finance, economics, politics, biology, sociology, gambling, and business
- Authored by Paul Wilmott, who brings three decades of experience in mathematics education to the subject
- Accessible introduction for anyone interested in understanding the foundations of machine learning
- Provides a practical approach to learning machine learning without overwhelming with complex details
- Suitable for readers who want to grasp the core concepts without delving too deep into theoretical aspects
| 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.
-
Data Science Beginners
Perfect for newcomers to data science who need to grasp mathematical principles underpinning machine learning techniques.
-
Professionals in Tech
Beneficial for tech professionals seeking to enhance their skills in machine learning through an applied mathematics lens.
-
Complete Beginners
Not suitable for individuals with no prior knowledge of mathematics or machine learning concepts.
Product Description
Machine Learning: An Applied Mathematics Introduction, (Paperback)
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Customer Questions & Answers
-
Question:
Who is the author of the book?
Answer: The author of the book is Paul Wilmott. -
Question:
What fields are covered in the real-world examples?
Answer: The real-world examples cover finance, economics, politics, biology, sociology, gambling, and business. -
Question:
Is the book suitable for beginners?
Answer: Yes, the book is an accessible introduction for anyone who wants to understand the foundations of machine learning.
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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Pros
- Accessible for general educational levels
- Covers essential mathematical concepts
- Well-structured for easy comprehension
- Perfect binding ensures durability
- Lightweight and portable format
Cons
- May lack advanced topics for experienced learners
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Features & Benefits
- 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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