Machine learning using R (Record no. 4447)
[ view plain ]
| 000 -LEADER | |
|---|---|
| fixed length control field | 04390nam a22002417a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20221207133823.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 221207b ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9789354246111 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.31 |
| Item number | KUM |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Kumar, Rahul |
| 245 ## - TITLE STATEMENT | |
| Title | Machine learning using R |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Name of publisher, distributor, etc. | Wiley India Pvt. Ltd. |
| Place of publication, distribution, etc. | New Delhi |
| Date of publication, distribution, etc. | 2022 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xii, 430 p. |
| 365 ## - TRADE PRICE | |
| Price type code | INR |
| Price amount | 779.00 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc. note | Table of content<br/><br/>Chapter 1 Machine Learning and R<br/><br/>1.1 Introduction to Analytics and Machine Learning<br/><br/>1.2 Why Machine Learning Algorithms?<br/><br/>1.3 Framework for Development of Machine Learning Algorithms<br/><br/>1.4 Introduction to R<br/><br/>1.5 Popularity Index for R<br/><br/>1.6 R Installations – Getting Started<br/><br/>1.7 RStudio<br/><br/>1.8 Understanding RStudio IDE<br/><br/>1.9 Anaconda<br/><br/>1.10 Anaconda Navigator<br/><br/>Chapter 2 Data Preprocessing<br/><br/>2.1 Data Preprocessing and Descriptive Analytics Using data.frame in R<br/><br/>2.2 IPL Dataset Description<br/><br/>2.3 Loading Dataset to R<br/><br/>2.4 Operations on data.table<br/><br/>2.5 Group by Operations<br/><br/>2.6 Keys and Binary Search-Based Subset<br/><br/>2.7 Join operations<br/><br/>2.8 Data Table, Data Frame, and Package DT<br/><br/>Chapter 3 Data Visualization<br/><br/>3.1 Data Visualization in R<br/><br/>3.2 IPL Dataset Description<br/><br/>3.3 Install Packages<br/><br/>3.4 Invoke packages<br/><br/>3.5 Exploration of Data Using Visualization<br/><br/>Chapter 4 Probability and Distributions<br/><br/>4.1 Overview<br/><br/>4.2 Probability Theory – Terminology<br/><br/>4.3 Random Variable<br/><br/>4.4 Binomial Distribution<br/><br/>4.5 Poisson Distribution<br/><br/>4.6 Exponential Distribution<br/><br/>4.7 Normal Distribution<br/><br/>4.8 Central Limit Theorem<br/><br/>4.9 Hypothesis Testing<br/><br/>4.10 Analysis of Variance (ANOVA)<br/><br/>Chapter 5 Supervised Learning Algorithm: Linear Regression<br/><br/>5.1 Simple Linear Regression<br/><br/>5.2 Steps for Regression Model Building<br/><br/>5.3 Building Simple Linear Regression Model<br/><br/>5.4 Example: Predicting MBA Salary from Marks in Grade 10<br/><br/>5.5 Model Diagnostics<br/><br/>5.6 Making Predictions and Measuring Accuracy<br/><br/>5.7 Multiple Linear Regression<br/><br/>5.8 Predicting the SOLD PRICE (Auction Price) of Players in Indian Premier League<br/><br/>5.9 Developing Multiple Linear Regression Model<br/><br/>5.10 Model Diagnostic<br/><br/>5.11 Making Predictions and Measuring Accuracy<br/><br/>Chapter 6 Classification Problems<br/><br/>6.1 Classification Overview<br/><br/>6.2 Binary Logistic Regression<br/><br/>6.3 Predicting Which Employee Will Leave the Organization Using Logistic Regression<br/><br/>6.4 Model Diagnostic<br/><br/>6.5 Gain Chart and Lift Chart<br/><br/>6.6 Decision Tree Learning 216<br/><br/>6.7 Model Selection<br/><br/>Chapter 7 Advanced Machine Learning<br/><br/>7.1 Overview<br/><br/>7.2 How Do Machines Learn?<br/><br/>7.3 Gradient Descent<br/><br/>7.4 Bias-Variance Trade-off<br/><br/>7.5 Strategy to Improve ML Models – Bias Variance Trade-off<br/><br/>7.6 Machine Learning – Regression Using glmnet Package<br/><br/>7.7 Machine Learning – Regression Using Caret<br/><br/>7.8 Results summary<br/><br/>7.9 Machine Learning – Logistic Regression Using glmnet<br/><br/>Chapter 8 Ensemble Methods<br/><br/>8.1 Overview<br/><br/>8.2 Random Forest<br/><br/>8.3 Class Imbalance Problem<br/><br/>8.4 Random Forest Using h2o Package<br/><br/>8.5 Gradient Boosted Machine – Regression<br/><br/>8.6 Gradient Boosting Machine – Classification<br/><br/>8.7 Gradient Boosting Machine using h2o Package<br/><br/>8.8 XGBoost using h2o Package<br/><br/>Chapter 9 Text Analytics<br/><br/>9.1 Overview<br/><br/>9.2 Data Scrapping<br/><br/>9.3 Regular Expression<br/><br/>9.4 Packages for Text Analytics<br/><br/>9.5 Data Import<br/><br/>9.6 Text Prepreprocessing<br/><br/>9.7 Twitter Data Visualization<br/><br/>9.8 Sentiment Analysis<br/><br/>9.9 Sentiment Analysis with Negative Words<br/><br/>9.10 Document Term Matrix<br/><br/>9.11 Topic Modelling |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Description<br/>Machine Learning Using R aims to make ML concepts and model development using R simpler for students and practitioners. This book covers the theoretical concepts behind ML algorithms and illustrates use of R for developing ML models using datasets from customer relationship management, healthcare, finance, human resource management, social media, and sports. The book discusses challenges and remedies in building machine learning models using several real-life cases which the authors have worked upon as a part of consulting engagements. |
| 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 | R (Computer program language) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Database management |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Electronic data processing |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Kumar, U. Dinesh |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Book |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Bill No | Bill Date | Home library | Current library | Shelving location | Date acquired | Source of acquisition | Cost, normal purchase price | Total Checkouts | Full call number | Accession Number | Checked out | Date last seen | Date checked out | Copy number | Cost, replacement price | Price effective from | Koha item type | Total Renewals |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | IT & Decisions Sciences | TB2161 | 12-11-2022 | Indian Institute of Management LRC | Indian Institute of Management LRC | General Stacks | 12/07/2022 | Technical Bureau India Pvt. Ltd. | 521.93 | 3 | 006.31 KUM | 003927 | 03/18/2024 | 12/19/2023 | 12/19/2023 | 1 | 779.00 | 12/07/2022 | Book | |||||
| Dewey Decimal Classification | IT & Decisions Sciences | TB2161 | 12-11-2022 | Indian Institute of Management LRC | Indian Institute of Management LRC | General Stacks | 12/07/2022 | Technical Bureau India Pvt. Ltd. | 521.93 | 1 | 006.31 KUM | 003928 | 04/21/2023 | 12/17/2022 | 2 | 779.00 | 12/07/2022 | Book | 1 |