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Applied Data Analytics - Principles and Applications

Johnson I. Agbinya
€52.99
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Artificial Neural Networks
Author_Johnson I. Agbinya
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big data methods
Binomial Random Variable
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Continuous Random Variables
Convolutional Neural Networks
COP=Denmark
Covariance Matrices
Covariance Matrix
Data Analytics
Data Set
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Digital Identity Management System
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evolutionary algorithms
Geometric Random Variable
health informatics analytics
Hidden Layer
HMM
Input Space
IoT Data
Kalman Filtering
Kalman Filters
Kalman Gain
Language_English
machine learning foundations
Markov Chain
MGF
Moment Generating Functions
Neural Network
Neural Networks
Output Layer
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Poisson Random Variable
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Principal Component Analysis
Probabilistic Neural Networks
Probability Generating Function
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Random Variable
real-time data analysis applications
Recurrent Neural Networks
ReLU
sensor data modeling
softlaunch
State Transition Matrix
State Transition Probabilities
statistical computing
Training Vector
Transition Probabilities
Vector Machines

Product details

  • ISBN 9788770043533
  • Weight: 453g
  • Dimensions: 156 x 234mm
  • Publication Date: 21 Oct 2024
  • Publisher: River Publishers
  • Publication City/Country: DK
  • Product Form: Paperback
  • Language: English
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Description

The emergence of huge amounts of data which require analysis and in some cases real-time processing has forced exploration into fast algorithms for handling very lage data sizes. Analysis of x-ray images in medical applications, cyber security data, crime data, telecommunications and stock market data, health records and business analytics data are but a few areas of interest. Applications and platforms including R, RapidMiner and Weka provide the basis for analysis, often used by practitioners who pay little to no attention to the underlying mathematics and processes impacting the data. This often leads to an inability to explain results or correct mistakes, or to spot errors.

Applied Data Analytics - Principles and Applications seeks to bridge this missing gap by providing some of the most sought after techniques in big data analytics. Establishing strong foundations in these topics provides practical ease when big data analyses are undertaken using the widely available open source and commercially orientated computation platforms, languages and visualisation systems. The book, when combined with such platforms, provides a complete set of tools required to handle big data and can lead to fast implementations and applications.

The book contains a mixture of machine learning foundations, deep learning, artificial intelligence, statistics and evolutionary learning mathematics written from the usage point of view with rich explanations on what the concepts mean. The author has thus avoided the complexities often associated with these concepts when found in research papers. The tutorial nature of the book and the applications provided are some of the reasons why the book is suitable for undergraduate, postgraduate and big data analytics enthusiasts.

This text should ease the fear of mathematics often associated with practical data analytics and support rapid applications in artificial intelligence, environmental sensor data modelling and analysis, health informatics, business data analytics, data from Internet of Things and deep learning applications.

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