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LAP Lambert Academic Publishing Machine Learning: Supervised,Unsupervised Learning,Ensemble and Probabilistic,Reinforcement Learning,Genetic Algorithms

Name: Machine Learning: Supervised,Unsupervised Learning,Ensemble and Probabilistic,Reinforcement Learning,Genetic Algorithms
Brand: LAP Lambert Academic Publishing
Price: 53.0 GBP
Availability: InStock

Network Topics

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Description

The purpose of machine learning is to discover patterns in your data and then make predictions based on often complex patterns to answer business questions, detect and analyse trends and help solve problems.This book provides a single source introduction to the primary approaches to machine learning. It is intended for advanced undergraduate and graduate students, as well as for developers and researchers in the field.The machine learning section begins with the basis of learning and the various association rule learning algorithms. Various types of learning like, reinforced, supervised, unsupervised and statistical are also included with numerous case studies and application exercises. The well explained algorithms and pseudo codes for each topic make this book useful for students.Machine learning techniques enable us to automatically extract features from data so as to solve predictive tasks, such as speech recognition, object recognition, machine translation, question-answering, anomaly detection, medical diagnosis and prognosis, automatic algorithm configuration, personalisation, robot control, time series forecasting.

Product Specifications

Brand: LAP Lambert Academic Publishing
Format: paperback
ASIN: 6204201093
Category: Books > Subjects > Computing & Internet > Networking & Security > Network Topics
Domain: Amazon UK
Release Date: 20 August 2021
Listed Since: 14 September 2021

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LAP Lambert Academic Publishing Machine Learning: Supervised,Unsupervised Learning,Ensemble and Probabilistic,Reinforcement Learning,Genetic Algorithms

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LAP Lambert Academic Publishing Machine Learning: Supervised,Unsupervised Learning,Ensemble and Probabilistic,Reinforcement Learning,Genetic Algorithms

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