東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Machine learning using R = with time...

Ramasubramanian, Karthik.

FindBook

Google Book

Amazon

博客來

Machine learning using R = with time series and industry-based use cases in R /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine learning using R/ by Karthik Ramasubramanian, Abhishek Singh.
其他題名:	with time series and industry-based use cases in R /
作者:	Ramasubramanian, Karthik.
其他作者:	Singh, Abhishek.
出版者:	Berkeley, CA :Apress : : 2019.,
面頁冊數:	xxiv, 700 p. :ill., digital ;24 cm.
內容註:	Chapter 1: Introduction to Machine Learning -- Chapter 2: Data Exploration and Preparation -- Chapter 3: Sampling and Resampling Techniques -- Chapter 4: Visualization of Data -- Chapter 5: Feature Engineering -- Chapter 6: Machine Learning Models: 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: Introduction to Deep Learning Models using Keras and TensorFlow.
Contained By:	Springer eBooks
標題:	Machine learning. -
電子資源:	https://doi.org/10.1007/978-1-4842-4215-5
ISBN:	9781484242155

Machine learning using R = with time series and industry-based use cases in R /
Ramasubramanian, Karthik.

Machine learning using Rwith time series and industry-based use cases in R /[electronic resource] :by Karthik Ramasubramanian, Abhishek Singh. - 2nd ed. - Berkeley, CA :Apress :2019. - xxiv, 700 p. :ill., digital ;24 cm.

Chapter 1: Introduction to Machine Learning -- Chapter 2: Data Exploration and Preparation -- Chapter 3: Sampling and Resampling Techniques -- Chapter 4: Visualization of Data -- Chapter 5: Feature Engineering -- Chapter 6: Machine Learning Models: 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: Introduction to Deep Learning Models using Keras and TensorFlow.

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.

ISBN: 9781484242155

Standard No.: 10.1007/978-1-4842-4215-5doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5 / .R363 2019

Dewey Class. No.: 006.31

Machine learning using R = with time series and industry-based use cases in R /
LDR:02570nmm a2200337 a 4500 001 2179155
003 DE-He213
005 20190719101634.0
006 m d
007 cr nn 008maaau
008 191122s2019 cau s 0 eng d
020 $a 9781484242155 $q (electronic bk.)
020 $a 9781484242148 $q (paper)
024 7 $a 10.1007/978-1-4842-4215-5 $2 doi
035 $a 978-1-4842-4215-5
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5 $b .R363 2019
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .R165 2019
100 1 $a Ramasubramanian, Karthik. $3 3219972
245 1 0 $a Machine learning using R $h [electronic resource] : $b with time series and industry-based use cases in R / $c by Karthik Ramasubramanian, Abhishek Singh.
250 $a 2nd ed.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2019.
300 $a xxiv, 700 p. : $b ill., digital ; $c 24 cm.
505 0 $a Chapter 1: Introduction to Machine Learning -- Chapter 2: Data Exploration and Preparation -- Chapter 3: Sampling and Resampling Techniques -- Chapter 4: Visualization of Data -- Chapter 5: Feature Engineering -- Chapter 6: Machine Learning Models: 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: Introduction to Deep Learning Models using Keras and TensorFlow.
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.
650 0 $a Machine learning. $3 533906
650 0 $a R (Computer program language) $3 784593
650 1 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Open Source. $3 2210577
650 2 4 $a Programming Languages, Compilers, Interpreters. $3 891123
700 1 $a Singh, Abhishek. $3 901091
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
856 4 0 $u https://doi.org/10.1007/978-1-4842-4215-5
950 $a Professional and Applied Computing (Springer-12059)