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- Publisher: States Academic Press
- ISBN-10: 1639893369
- ISBN-13: 9781639893362
- Format: 21.6 x 27.9 x 1.4 cm, hardcover
- Language: English
- SAVE -10% with code: EXTRA
Machine Learning: Concepts, Tools and Techniques (e-book) (used book) | bookbook.eu
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Description
The study of computer algorithms that improve automatically through experience and by the use of data is referred to as machine learning. It is considered to be a part of artificial intelligence. Data mining is a related area of study, focusing on exploratory data analysis through unsupervised learning. Performing machine learning involves creating a model. Some of the different types of models that are used and researched for machine learning systems are artificial neural networks, decision trees, support vector machines, regression analysis, Bayesian networks and genetic algorithms. There are many applications for machine learning such as agriculture, banking, economics, marketing, medical diagnosis, telecommunication, software engineering, time series forecasting and bioinformatics. As this field is emerging at a rapid pace, the contents of this book will help the readers understand the modern concepts and applications of the subject. It provides comprehensive insights into the field of machine learning. This book is a collective contribution of a renowned group of international experts.
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- Publisher: States Academic Press
- ISBN-10: 1639893369
- ISBN-13: 9781639893362
- Format: 21.6 x 27.9 x 1.4 cm, hardcover
- Language: English English
The study of computer algorithms that improve automatically through experience and by the use of data is referred to as machine learning. It is considered to be a part of artificial intelligence. Data mining is a related area of study, focusing on exploratory data analysis through unsupervised learning. Performing machine learning involves creating a model. Some of the different types of models that are used and researched for machine learning systems are artificial neural networks, decision trees, support vector machines, regression analysis, Bayesian networks and genetic algorithms. There are many applications for machine learning such as agriculture, banking, economics, marketing, medical diagnosis, telecommunication, software engineering, time series forecasting and bioinformatics. As this field is emerging at a rapid pace, the contents of this book will help the readers understand the modern concepts and applications of the subject. It provides comprehensive insights into the field of machine learning. This book is a collective contribution of a renowned group of international experts.
Reviews