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- Author: Ramesh Annabathula
- Publisher: VDM Verlag
- ISBN-10: 3639070577
- ISBN-13: 9783639070576
- Format: 15.2 x 22.9 x 0.7 cm, minkšti viršeliai
- Language: English
- SAVE -10% with code: EXTRA
A Web Tool For Crime Data Analysis Data Analysis - A Machine Learning Algorithm Approach (e-book) (used book) | bookbook.eu
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Description
In the modern age, information is a crucial aspect of management. It is an important source needed to develop other resources and it is believed that information is power. The development and use of information management systems is a modern phenomenon concerned with the use of appropriate information that will lead to better planning and better decision making. A web tool for crime data analysis is a comprehensive textbook on such subject, covering a broad array of topics not usually included in practical machine learning algorithm text books. It discusses many methods based in different fields, including statistics, pattern recognition, neural networks, artificial intelligence, and data mining, in order to present a practical treatment of machine learning problems and solutions. All learning algorithms are explained so that anybody can easily understand the concepts and transfer into a computer program.The book can be used by undergraduates, graduates and engineering students who have completed courses in computer programming, probability, calculus, and linear algebr
EXTRA 10 % discount with code: EXTRA
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- Author: Ramesh Annabathula
- Publisher: VDM Verlag
- ISBN-10: 3639070577
- ISBN-13: 9783639070576
- Format: 15.2 x 22.9 x 0.7 cm, minkšti viršeliai
- Language: English English
In the modern age, information is a crucial aspect of management. It is an important source needed to develop other resources and it is believed that information is power. The development and use of information management systems is a modern phenomenon concerned with the use of appropriate information that will lead to better planning and better decision making. A web tool for crime data analysis is a comprehensive textbook on such subject, covering a broad array of topics not usually included in practical machine learning algorithm text books. It discusses many methods based in different fields, including statistics, pattern recognition, neural networks, artificial intelligence, and data mining, in order to present a practical treatment of machine learning problems and solutions. All learning algorithms are explained so that anybody can easily understand the concepts and transfer into a computer program.The book can be used by undergraduates, graduates and engineering students who have completed courses in computer programming, probability, calculus, and linear algebr
Reviews