Machine learning (Record no. 5008)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 05059nam a22001937a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20230322104219.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 230322b ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9788126578511 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.31 |
| Item number | SRI |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Srinivasaraghavan, Anuradha |
| 245 ## - TITLE STATEMENT | |
| Title | Machine learning |
| 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. | 2023 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xix, 308 p. |
| 365 ## - TRADE PRICE | |
| Price type code | INR |
| Price amount | 619.00 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc. note | Table of content<br/>Part 1 Basics of Machine Learning<br/><br/>Chapter 1 Introduction to Machine Learning<br/><br/>1.1 What is Machine Learning?<br/><br/>1.2 Where is Machine Learning Used?<br/><br/>1.3 Applications of Machine Learning<br/><br/>1.4 Types of Machine Learning<br/><br/><br/>Chapter 2 Model and Cost Function<br/><br/>2.1 Introduction<br/><br/>2.2 Representation of a Model<br/><br/>2.3 Cost Function Notation for Measuring the Accuracy of a Hypothesis Function<br/><br/>2.4 Measuring Accuracy of a Hypothesis Function<br/><br/>2.5 Minimizing the Cost Function for a Single-Variable Function<br/><br/>2.6 Minimizing the Cost Function for a Two-Variable Function<br/><br/>2.7 Role of Gradient Function in Minimizing a Cost Function<br/><br/>Chapter 3 Basics of Vectors and Matrices<br/><br/>3.1 Introduction<br/><br/>3.2 Notations<br/><br/>3.3 Types of Matrices<br/><br/>3.4 Matrix Operations<br/><br/>3.5 Determinant of a Matrix<br/><br/>3.6 Inverse of a Matrix<br/><br/>Chapter 4 Basics of Python<br/><br/>4.1 Introduction<br/><br/>4.2 Installing Python<br/><br/>4.3 Anaconda<br/><br/>4.4 Running Jupyter Notebook<br/><br/>4.5 Python 3: Basic Syntax<br/><br/>4.6 Python Identifiers<br/><br/>4.7 Basic Operators in Python<br/><br/>4.8 Python Decision-Making<br/><br/>4.9 Python Loops<br/><br/>4.10 Numerical Python (NumPy)<br/><br/>4.11 NumPy Matplotlib<br/><br/>4.12 Introduction to Pandas<br/><br/>4.13 Introduction to Scikit-Learn<br/><br/>Chapter 5 Data Preprocessing<br/><br/>5.1 Overview of Data Preprocessing<br/><br/>5.2 Data Cleaning<br/><br/>5.3 Data Integration <br/><br/>5.4 Data Transformation <br/><br/>5.5 Data Reduction or Dimensionality Reduction <br/><br/>Part 2 Supervised Learning Algorithms<br/><br/>Chapter 6 Artificial Neural Networks<br/><br/>6.1 Introduction<br/><br/>6.2 Evolution of Neural Networks<br/><br/>6.3 Biological Neuron<br/><br/>6.4 Basics of Artificial Neural Networks<br/><br/>6.5 Activation Functions<br/><br/>6.6 McCulloch–Pitts Neuron Model<br/><br/>Chapter 7 Linear Regression<br/><br/>7.1 Introduction to Supervised Learning and Regression<br/><br/>7.2 Statistical Relation between Two Variables and Scatter Plots<br/><br/>7.3 Steps to Establish a Linear Regression<br/><br/>7.4 Evaluation of Model Estimators<br/><br/>7.5 Solved Problems on Linear Regression<br/><br/>Chapter 8 Logistic Regression<br/><br/>8.1 Introduction to Logistic Regression<br/><br/>8.2 Scenarios Which Require Logistic Regression<br/><br/>8.3 Odds<br/><br/>8.4 Building Logistic Regression Model (Logit Function)<br/><br/>8.5 Maximum Likelihood Estimation<br/><br/>8.6 Example of Logistic Regression<br/><br/>Chapter 9 Decision Tree<br/><br/>9.1 Introduction to Classification and Decision Tree<br/><br/>9.2 Problem Solving Using Decision Trees<br/><br/>9.3 Basic Decision Tree Learning Algorithm<br/><br/>9.4 Popularity of Decision Tree Classifiers<br/><br/>9.5 Steps to Construct a Decision Tree<br/><br/>9.6 Classification Using Decision Trees<br/><br/>9.7 Issues in Decision Trees<br/><br/>9.8 Rule-Based Classification<br/><br/>9.9 Pruning the Rule Set<br/><br/>Chapter 10 Support Vector Machines<br/><br/>10.1 Introduction to Support Vector Machines<br/><br/>10.2 Linear Support Vector Machines<br/><br/>10.3 Optimal Hyperplane<br/><br/>10.4 Basics of Vectors<br/><br/>10.5 Radial Basis Functions<br/><br/>Chapter 11 Bayesian Classification<br/><br/>11.1 Introduction to Bayesian Classifiers<br/><br/>11.2 Naive Bayes Classifier<br/><br/>11.3 Bayesian Belief Networks<br/><br/>11.4 k-Nearest Neighbor (KNN)<br/><br/>11.5 Measuring Classifier Accuracy<br/><br/>Chapter 12 Hidden Markov Model<br/><br/>12.1 Introduction to Hidden Markov Model<br/><br/>12.2 Issues in Hidden Markov Model<br/><br/>Part 3 Unsupervised Algorithms<br/><br/>Chapter 13 Introduction to Unsupervised Learning Algorithms<br/><br/>13.1 Introduction to Clustering<br/><br/>13.2 Types of Clustering<br/><br/>13.3 Partitioning Methods of Clustering<br/><br/>13.4 Hierarchical Methods<br/><br/>Part 4 Optimization Techniques<br/><br/>Chapter 14 Optimization<br/><br/>14.1 Introduction to Optimization<br/><br/>14.2 Classification of Optimization Problems<br/><br/>14.3 Linear vs Nonlinear Programming Problems<br/><br/>14.4 Unconstrained Minimization Problems<br/><br/>14.5 Gradient-Based Methods (Descent Methods)<br/><br/>14.6 Introduction to Derivative-Free Optimization<br/><br/>14.7 Derivative-Based vs Derivative-Free Optimization<br/><br/> |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | This book offers the readers the basics of machine learning in a very simple, user-friendly language. While browsing the Table of Contents, you will realize that you are given an introduction to every concept that comes under the umbrella of machine learning. This book is aimed at students who are new to the topic of machine learning. It is meant for students studying machine learning in their undergraduate and postgraduate courses in information technology. It is also aimed at computer engineering students. It will help familiarize students with the terms and terminologies used in machine learning. We hope that this book serves as an entry point for students to pursue their future studies and careers in machine learning. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Machine learning |
| 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 | Date last seen | Date checked out | Copy number | Cost, replacement price | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | IT & Decisions Sciences | TB3162 | 16-02-2023 | Indian Institute of Management LRC | Indian Institute of Management LRC | General Stacks | 03/22/2023 | Technical Bureau India Pvt. Ltd. | 433.30 | 3 | 006.31 SRI | 004867 | 10/27/2023 | 10/27/2023 | 1 | 619.00 | 03/22/2023 | Book |