Introduction to machine learning with R : (Record no. 65269)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01997nam a22002057a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 200605b2018 ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9789352137251 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.31 BUR-S |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Burger, Scott V. |
| 245 ## - TITLE STATEMENT | |
| Title | Introduction to machine learning with R : |
| Remainder of title | rigorous mathematical analysis / |
| Statement of responsibility, etc. | Scott V. Burger |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc. | India |
| Name of publisher, distributor, etc. | Shorff Publishers & Distributors |
| Date of publication, distribution, etc. | 2018 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 212 p. |
| 365 ## - TRADE PRICE | |
| Price type code | INR |
| Price amount | 650.00. |
| 500 ## - GENERAL NOTE | |
| General note | Machine learning is an intimidating subject until you know the fundamentals. If you understand basic coding concepts, this introductory guide will help you gain a solid foundation in machine learning principles. Using the R programming language, you’ll first start to learn with regression modelling and then move into more advanced topics such as neural networks and tree-based methods.<br/><br/>Finally, you’ll delve into the frontier of machine learning, using the caret package in R. Once you develop a familiarity with topics such as the difference between regression and classification models, you’ll be able to solve an array of machine learning problems. Author Scott V. Burger provides several examples to help you build a working knowledge of machine learning.<br/><br/>Explore machine learning models, algorithms, and data training<br/>Understand machine learning algorithms for supervised and unsupervised cases<br/>Examine statistical concepts for designing data for use in models<br/>Dive into linear regression models used in business and science<br/>Use single-layer and multilayer neural networks for calculating outcomes<br/>Look at how tree-based models work, including popular decision trees<br/>Get a comprehensive view of the machine learning ecosystem in R<br/>Explore the powerhouse of tools available in R’s caret package |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | R (Computer program language) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Machine learning |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Statistics--Data processing |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Mathematical statistics--Data processing |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Neural networks (Computer science) |
| 952 ## - LOCATION AND ITEM INFORMATION (KOHA) | |
| Withdrawn status | |
| Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Home library | Current library | Shelving location | Date acquired | Cost, normal purchase price | Total Checkouts | Full call number | Barcode | Date last seen | Date last checked out | Price effective from | Koha item type |
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| Dewey Decimal Classification | 003-007 | BITS Pilani Hyderabad | BITS Pilani Hyderabad | General Stack (For lending) | 05/06/2020 | 650.00 | 10 | 006.31 BUR-S | 41093 | 30/10/2024 | 21/10/2024 | 05/06/2020 | Books |