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Simpler : Using Machine Learning Algorithms in R
Published by CreateSpace Independent Publishing Platform, 2017
ISBN 10: 1546324194 ISBN 13: 9781546324195
Language: English
Condition: As New. Unread book in perfect condition.
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Machine Learning Using TensorFlow Cookbook: Create powerful machine learning algorithms with TensorFlow
Published by Packt Publishing (edition ), 2021
ISBN 10: 1800208863 ISBN 13: 9781800208865
Language: English
Paperback. Condition: Very Good. Ship within 24hrs. Satisfaction 100% guaranteed. APO/FPO addresses supported.
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Paperback or Softback. Condition: New. Machine Learning Brick by Brick, Epoch 1: Using LEGO(R) to Teach Concepts, Algorithms, and Data Structures 0.09. Book.
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paperback. Condition: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
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US$ 18.35
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Hands-On Machine Learning for Algorithmic Trading: Design and implement investment strategies based on smart algorithms that learn from data using Python
Published by Packt Publishing (edition ), 2018
ISBN 10: 178934641X ISBN 13: 9781789346411
Language: English
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Machine Learning Algorithms: A step-by-step guide to all Machine Learning algorithms with implementation using Python!
Published by Clever Fox Publishing, 2023
ISBN 10: 935648483X ISBN 13: 9789356484832
Language: English
Condition: New. pp. 234.
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PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.
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Machine Learning with R - Lab Manual: Step-by-Step guide for executing Machine Learning algorithms using R
Published by Independently published, 2020
ISBN 10: 1703525019 ISBN 13: 9781703525014
Language: English
US$ 15.81
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Simply: Basic Machine Learning Algorithms Using Python
Published by Independently published, 2019
ISBN 10: 1071450212 ISBN 13: 9781071450215
Language: English
US$ 17.45
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Condition: very_good. Book is in very good condition and may include minimal underlining highlighting. The book can also include "From the library of" labels. May not contain miscellaneous items toys, dvds, etc. . We offer 100% money back guarantee and 24 7 customer service.
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Data Analytics For Businesses 2019: How to Master Data Science with Optimized Marketing Strategies using Data Mining Algorithms, Big Data for Business and Machine Learning
Published by Independently published, 2019
ISBN 10: 1079434984 ISBN 13: 9781079434989
Language: English
US$ 27.76
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Add to basketPaperback. Condition: Brand New. 211 pages. 8.00x5.00x0.48 inches. In Stock.
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Ensemble Machine Learning using R: A beginner's guide to combining the power of machine learning algorithms using ensemble techniques
Published by Packt Publishing - ebooks Account, 2018
ISBN 10: 1788624149 ISBN 13: 9781788624145
Language: English
Seller: Zubal-Books, Since 1961, Cleveland, OH, U.S.A.
Seller rating 5 out of 5 stars
Condition: New. *Price HAS BEEN REDUCED by 10% until Monday, Aug. 11 (SALE ITEM)* 526 pp., paperback, new. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country.
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Building a Recommendation Engine with Machine Learning: Developing personalized recommendation systems using algorithms like collaborative filtering a
ISBN 13: 9798289992628
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Condition: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.
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Breast Cancer Prediction Using Machine Learning Algorithms
Published by LAP LAMBERT Academic Publishing, 2023
ISBN 10: 6206686523 ISBN 13: 9786206686521
Language: English
Condition: New.
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Machine Learning Guide for Oil and Gas Using Python: A Step-by-Step Breakdown with Data, Algorithms, Codes, and Applications
Published by Gulf Professional Publishing, 2021
ISBN 10: 0128219297 ISBN 13: 9780128219294
Language: English
Seller: Amazing Books Pittsburgh, Pittsburgh, PA, U.S.A.
Seller rating 4 out of 5 stars
paperback. Condition: Very Good. Excellent condition copy, minimal wear, pages are clean and unmarked. Some mild wear along bottom of covers and exterior spine. Paperback. LW.
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US$ 69.35
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Algorithms with JULIA: Optimization, Machine Learning, and Differential Equations Using the JULIA Language
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
US$ 63.17
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Prediction of Epidemic Diseases Using Machine Learning Algorithms : Machine Learning
Published by LAP LAMBERT Academic Publishing, 2020
ISBN 10: 3330008865 ISBN 13: 9783330008861
Language: English
US$ 34.48
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Add to basketCondition: Hervorragend. Zustand: Hervorragend | Seiten: 68 | Sprache: Englisch | Produktart: Bücher.
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Crying emotion classification, using machine learning algorithms
Published by Éditions universitaires européennes, 2019
ISBN 10: 6138459857 ISBN 13: 9786138459859
Language: English
US$ 35.27
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Electronic Banking Fraud Detection: Using Data Mining Techniques And R Software For Implementing Machine Learning Algorithms In Prevention Of Fraud
Published by LAP LAMBERT Academic Publishing, 2017
ISBN 10: 3659916870 ISBN 13: 9783659916878
Language: English
US$ 77.40
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Add to basketPaperback. Condition: Brand New. 80 pages. 8.66x5.91x0.19 inches. In Stock.
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US$ 57.42
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Add to basketTaschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Doctoral Thesis / Dissertation from the year 2017 in the subject Engineering - Computer Engineering, grade: 100.00/100.00, Çukurova University, language: English, abstract: The purpose of this thesis is twofold. The first purpose is to develop new hybrid feature selection-based maximal oxygen uptake (VO2max) prediction models using for the first time the double and triple combinations of maximal, submaximal and questionnaire variables. Several machine learning methods including Support Vector Machine, artificial neural network-based and tree-structured methods combined individually with three feature selectors Relief-F, minimum redundancy maximum relevance (mRMR) and maximum-likelihood feature selector (MLFS) have been applied for model development. The second purpose is to design a new ensemble feature selector, which aggregates the consensus properties of Relief-F, mRMR and MLFS to produce more robust decisions about the set of relevantly identified VO2max predictors and to create more accurate prediction models. Using 10-fold cross validation on three different datasets, the performance of prediction models has been evaluated by calculating their multiple correlation coefficients (R¿s) and root mean squared errors (RMSE¿s). The results show that compared with the results of the other regular feature selection-based models in literature, the reported values of R and RMSE of the hybrid models in this thesis are considerably more accurate. Furthermore, prediction models based on the proposed ensemble feature selector outperform the models created by individually using the Relief-F, mRMR or MLFS, achieving similar or ideally up to 12.46% lower error rates on the average.
