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Nº de artículo:
43340608
Machine Learning: An Applied Mathematics Introduction, (Paperback)
94% of respondents would recommend this to a friend
Nº de artículo:
43340608
GTQ 244
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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.
Detalles de producto
- 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.
DESCRIPCIÓN DEL PRODUCTO
Machine Learning: An Applied Mathematics Introduction, (Paperback)
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Preguntas y respuestas de los clientes
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Pregunta:
Who is the author of the book?
Respuesta: The author of the book is Paul Wilmott. -
Pregunta:
What fields are covered in the real-world examples?
Respuesta: The real-world examples cover finance, economics, politics, biology, sociology, gambling, and business. -
Pregunta:
Is the book suitable for beginners?
Respuesta: Yes, the book is an accessible introduction for anyone who wants to understand the foundations of machine learning.
Paul Wilmott All Books Editorial Review
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ventajas
- Comprehensive learning resource
- Clear explanations of concepts
- Practical applications included
- Great for beginners and experts
- Engaging examples and exercises
Contras
- Some topics may be too advanced for beginners.
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características y beneficios
- 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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