Introduction to Machine Learning with R: Rigorous Mathematical Analysis
Machine learning can be a difficult subject if you're not familiar with the basics. With this book, you'll get a solid foundation of introductory principles used in machine learning with the statistical programming language R. You'll start with the basics like regression, then move in...
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
New Delhi
Shroff Publishers & Distributors Pvt. Ltd.
2019
|
| Subjects: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
MARC
| LEADER | 00000nam a22000007a 4500 | ||
|---|---|---|---|
| 008 | 200314b ||||| |||| 00| 0 eng d | ||
| 952 | |0 0 |1 0 |2 ddc |4 0 |6 006_310000000000000_BUR |7 0 |9 11450 |a 50008178 |b 50008178 |d 2020-02-13 |e The Book Syndicate Hyderabad |g 650.00 |l 0 |o 006.31 BUR |p J4700 |r 2020-03-14 00:00:00 |w 2020-03-14 |y BK | ||
| 999 | |c 6349 |d 6349 | ||
| 020 | |a 9789352137251 | ||
| 082 | |a 006.31 |b BUR | ||
| 100 | |a Burger, Scott V. |9 6766 | ||
| 245 | |a Introduction to Machine Learning with R: Rigorous Mathematical Analysis | ||
| 260 | |a New Delhi |b Shroff Publishers & Distributors Pvt. Ltd. |c 2019 | ||
| 300 | |a ix, 213 p. | ||
| 520 | |a Machine learning can be a difficult subject if you're not familiar with the basics. With this book, you'll get a solid foundation of introductory principles used in machine learning with the statistical programming language R. You'll start with the basics like regression, then move into more advanced topics like neural networks, and finally delve into the frontier of machine learning in the R world with packages like Caret. By developing a familiarity with topics like understanding the difference between regression and classification models, you'll be able to solve an array of machine learning problems. Knowing when to use a specific model or not can mean the difference between a highly accurate model and a completely useless one. This book provides copious examples to build a working knowledge of machine learning. Understand the major parts of machine learning algorithms Recognize how machine learning can be used to solve a problem in a simple manner Figure out when to use certain machine learning algorithms versus others Learn how to operationalize algorithms with cutting edge packages | ||
| 650 | |a R - Computer Program Language |9 6767 | ||
| 650 | |a Machine Learning |9 6768 | ||
| 650 | |a Statistics - Data Processing |9 6769 | ||