Machine learning using R : with time series and industry-based uses in R

Examine the latest technological advancements in building a scalable machine-learning model with big data using R. This second edition shows you how to work with a machine-learning algorithm and use it to build a ML model from raw data. You will see how to use R programming with TensorFlow, thus avo...

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Bibliographic Details
Main Authors: Ramasubramanian, Karthik, (Author), Singh, Abhishek, 1976- (Author)
Format: eBook
Language: English
Published: [New York, New York] : Apress, [2019]
Edition: Second edition.
Subjects:
Machine learning.
R (Computer program language)
elektronické knihy
electronic books
ISBN: 9781484242155
1484242157
9781484242148
1484242149
Physical Description: 1 online resource (xxiv, 700 pages)

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Table of contents

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020 |z 1484242149 
035 |a (OCoLC)1080081706  |z (OCoLC)1079917856 
100 1 |a Ramasubramanian, Karthik,  |e author. 
245 1 0 |a Machine learning using R :  |b with time series and industry-based uses in R /  |c Karthik Ramasubramanian, Abhishek Singh. 
250 |a Second edition. 
264 1 |a [New York, New York] :  |b Apress,  |c [2019] 
300 |a 1 online resource (xxiv, 700 pages) 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
505 0 |a Chapter 1: Introduction to Machine Learning and R -- Chapter 2: Data Exploration and Preparation -- Chapter 3: Sampling and Resampling Techniques -- Chapter 4: Data Visualization in R -- Chapter 5: Feature Engineering -- Chapter 6: Machine Learning Theory and Practice -- Chapter 7: Machine Learning Model Evaluation -- Chapter 8: Model Performance Improvement -- Chapter 9: Time Series Modelling -- Chapter 10: Scalable Machine Learning and related technology -- Chapter 11: Deep Learning Models using Keras and TensorFlow. 
506 |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty 
520 |a Examine the latest technological advancements in building a scalable machine-learning model with big data using R. This second edition shows you how to work with a machine-learning algorithm and use it to build a ML model from raw data. You will see how to use R programming with TensorFlow, thus avoiding the effort of learning Python if you are only comfortable with R. As in the first edition, the authors have kept the fine balance of theory and application of machine learning through various real-world use-cases which gives you a comprehensive collection of topics in machine learning. New chapters in this edition cover time series models and deep learning. You will: Understand machine learning algorithms using R Master the process of building machine-learning models Cover the theoretical foundations of machine-learning algorithms See industry focused real-world use cases Tackle time series modeling in R Apply deep learning using Keras and TensorFlow in R. 
590 |a Knovel  |b Knovel (All titles) 
650 0 |a Machine learning. 
650 0 |a R (Computer program language) 
655 7 |a elektronické knihy  |7 fd186907  |2 czenas 
655 9 |a electronic books  |2 eczenas 
700 1 |a Singh, Abhishek,  |d 1976-  |e author.  |1 https://id.oclc.org/worldcat/entity/E39PCjrQrFgkg8XJwCvTybBvDy 
776 0 8 |i Print version:  |a Ramasubramanian, Karthik.  |t Machine Learning Using R : With Time Series and Industry-Based Use Cases in R.  |d Berkeley, CA : Apress L.P., ©2018  |z 9781484242148 
856 4 0 |u https://proxy.k.utb.cz/login?url=https://app.knovel.com/hotlink/toc/id:kpMLURWTS3/machine-learning-using?kpromoter=marc  |y Full text 

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