Applied Machine Learning for Smart Data Analysis
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advanced machine learning applications
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B01=Mohd. Shafi Pathan
B01=Nilanjan Dey
B01=Parikshit N. Mahalle
B01=Sanjeev Wagh
Category1=Non-Fiction
Category=UNF
Category=UYQM
computational linguistics
COP=United Kingdom
data analytics
Data Set
De-noised Image
deep learning
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Dynamic Control Structure
educational data mining
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fuzzy clustering algorithm
Fuzzy Partition Matrix
Haar Transform
Haar Wavelet
Haar Wavelet Filters
IBM Model
Image Quality Assessment
Internet of Things
JSON Object
Language_English
LDA
machine learning
Machine Learning Models
Malicious Application
Malware Detection
mobile computing security
NEs
PA=Temporarily unavailable
Plagiarism Evaluation
Price_€100 and above
PS=Active
psychotherapist chatbot
RF
semantic data analysis
softlaunch
Spam Classification
Supervised Machine Learning
Training Data Set Size
Unsupervised Machine Learning
VANET
Wavelet Coefficients
Wavelet Transform
Product details
- ISBN 9781138339798
- Weight: 520g
- Dimensions: 156 x 234mm
- Publication Date: 05 Jun 2019
- Publisher: Taylor & Francis Ltd
- Publication City/Country: GB
- Product Form: Hardback
- Language: English
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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
Nilanjan Dey, Sanjeev Wagh, Parikshit N. Mahalle, Mohd. Shafi Pathan
