Machine Learning | Supervised,Unsupervised Learning,Ensemble and Probabilistic,Reinforcement Learning,Genetic Algorithms

G. Krishna Kumari

ISBN 10: 6204201093 / ISBN 13: 9786204201092

Published by LAP LAMBERT Academic Publishing, 2021

About this Item

Machine Learning | Supervised,Unsupervised Learning,Ensemble and Probabilistic,Reinforcement Learning,Genetic Algorithms | G. Krishna Kumari | Taschenbuch | Englisch | 2021 | LAP LAMBERT Academic Publishing | EAN 9786204201092 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Seller Inventory # 120560168

Bibliographic Details

• Title: Machine Learning | Supervised,Unsupervised Learning,Ensemble and Probabilistic,Reinforcement Learning,Genetic Algorithms
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2021
• Language: English
• ISBN 10: 6204201093
• ISBN 13: 9786204201092
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 9D millimeters 220 millimeters width by 150 millimeters height by 9 millimeters depth
• Weight: 209 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

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.