Applied Machine Learning for Smart Data Analysis
Nonfiction, Computers, Advanced Computing, Theory, Science & Nature, Technology, Electricity, Database Management
| Author: | ISBN: | 9780429804564 | |
| Publisher: | CRC Press | Publication: | May 20, 2019 |
| Imprint: | CRC Press | Language: | English |
| Author: | |
| ISBN: | 9780429804564 |
| Publisher: | CRC Press |
| Publication: | May 20, 2019 |
| Imprint: | CRC Press |
| Language: | English |
The book focuses on how machine learning and the Internet of Things (IoT) has empowered the advancement of information driven arrangements including key concepts and advancements. Ontologies that are used in heterogeneous IoT environments have been discussed including interpretation, context awareness, analyzing various data sources, machine learning algorithms and intelligent services and applications. Further, it includes unsupervised and semi-supervised machine learning techniques with study of semantic analysis and thorough analysis of reviews. Divided into sections such as machine learning, security, IoT and data mining, the concepts are explained with practical implementation including results.
Key Features
- Follows an algorithmic approach for data analysis in machine learning
- Introduces machine learning methods in applications
- Address the emerging issues in computing such as deep learning, machine learning, Internet of Things and data analytics
- Focuses on machine learning techniques namely unsupervised and semi-supervised for unseen and seen data sets
- Case studies are covered relating to human health, transportation and Internet applications
The book focuses on how machine learning and the Internet of Things (IoT) has empowered the advancement of information driven arrangements including key concepts and advancements. Ontologies that are used in heterogeneous IoT environments have been discussed including interpretation, context awareness, analyzing various data sources, machine learning algorithms and intelligent services and applications. Further, it includes unsupervised and semi-supervised machine learning techniques with study of semantic analysis and thorough analysis of reviews. Divided into sections such as machine learning, security, IoT and data mining, the concepts are explained with practical implementation including results.
Key Features
- Follows an algorithmic approach for data analysis in machine learning
- Introduces machine learning methods in applications
- Address the emerging issues in computing such as deep learning, machine learning, Internet of Things and data analytics
- Focuses on machine learning techniques namely unsupervised and semi-supervised for unseen and seen data sets
- Case studies are covered relating to human health, transportation and Internet applications
