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DATA SCIENCE AND MACHINE LEARNING IN R
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Overview of DATA SCIENCE AND MACHINE LEARNING IN R Book
| Title | DATA SCIENCE AND MACHINE LEARNING IN R |
| Author | Reema Thareja |
| ISBN-13 | 9789354600333 |
| Publication | MHE |
The book has been designed keeping in mind the needs of the beginners of this subject area while having no prior knowledge in this field. It is aimed to be used as a textbook for undergraduate and postgraduate students. However, it can also be used by research scholars and professionals. The text introduces the concepts of R programming language in a lucid way and enables the reader to use these to perform data science and machine learning applications for solving real-world problems. Every br>Chapter in this book contains multiple programming exercises and examples that enhance the understanding of the subject. salient features: 1) discussion on key topics such as ggplot2 package, sentiment analysis, web scrapping, etc. 2) Easy explanation of concepts along with programming applications 3) wide variety of pedagogical elements such as br>Chapter highlights, review exercises, MCQs, programming exercises, etc. 4) teaching aids such as ‘programming tips’ to help students remember important concepts and identify some typical errors 5) additional appendices and case studies as online resources.
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Overview of DATA SCIENCE AND MACHINE LEARNING IN R Book
The book has been designed keeping in mind the needs of the beginners of this subject area while having no prior knowledge in this field. It is aimed to be used as a textbook for undergraduate and postgraduate students. However, it can also be used by research scholars and professionals. The text introduces the concepts of R programming language in a lucid way and enables the reader to use these to perform data science and machine learning applications for solving real-world problems. Every br>Chapter in this book contains multiple programming exercises and examples that enhance the understanding of the subject. salient features: 1) discussion on key topics such as ggplot2 package, sentiment analysis, web scrapping, etc. 2) Easy explanation of concepts along with programming applications 3) wide variety of pedagogical elements such as br>Chapter highlights, review exercises, MCQs, programming exercises, etc. 4) teaching aids such as ‘programming tips’ to help students remember important concepts and identify some typical errors 5) additional appendices and case studies as online resources.
Book Type :
Artificial Intelligence
Book Binding :
Not Available
Language :
English
No. of Pages :
496 Pages
ISBN - 10 :
9354600336
ISBN - 13 :
9789354600333
DATA SCIENCE AND MACHINE LEARNING IN R Published On :
30 July 2021
Publisher :
McGraw Hill
Edition :
1st
About Reema Thareja:
Reema Thareja is the author of DATA SCIENCE AND MACHINE LEARNING IN R.
Disclaimer:
- DATA SCIENCE AND MACHINE LEARNING IN R book is not for reading online or for free download in PDF or eBook format.
- Table of Contents, Index, Syllabus, summary and image of DATA SCIENCE AND MACHINE LEARNING IN R may be of a different edition or of the same title.
- Price can change due to reprinting, price change by publisher or sourcing cost change for imported books.
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Overview of DATA SCIENCE AND MACHINE LEARNING IN R Book
| Title | DATA SCIENCE AND MACHINE LEARNING IN R |
| Author | Reema Thareja |
| ISBN-13 | 9789354600333 |
| Publication | MHE |
The book has been designed keeping in mind the needs of the beginners of this subject area while having no prior knowledge in this field. It is aimed to be used as a textbook for undergraduate and postgraduate students. However, it can also be used by research scholars and professionals. The text introduces the concepts of R programming language in a lucid way and enables the reader to use these to perform data science and machine learning applications for solving real-world problems. Every br>Chapter in this book contains multiple programming exercises and examples that enhance the understanding of the subject. salient features: 1) discussion on key topics such as ggplot2 package, sentiment analysis, web scrapping, etc. 2) Easy explanation of concepts along with programming applications 3) wide variety of pedagogical elements such as br>Chapter highlights, review exercises, MCQs, programming exercises, etc. 4) teaching aids such as ‘programming tips’ to help students remember important concepts and identify some typical errors 5) additional appendices and case studies as online resources.