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Real-world machine learning (Record no. 5016)

MARC details
000 -LEADER
fixed length control field	03710nam a22002417a 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field	20230322120319.0
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	9789351199496
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number	006.31
Item number	BRI
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name	Brink, Henrik
245 ## - TITLE STATEMENT
Title	Real-world machine learning
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher, distributor, etc.	Dreamtech Publisher
Place of publication, distribution, etc.	New Delhi
Date of publication, distribution, etc.	2020
300 ## - PHYSICAL DESCRIPTION
Extent	xxii, 242 p.
365 ## - TRADE PRICE
Price type code	INR
Price amount	799.00
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc. note	Table of content<br/>1. What is machine learning?<br/><br/>1.1 Understanding how machines learn<br/><br/>1.2 Using data to make decisions<br/><br/>1.3 Following the ML workflow: from data to deployment<br/><br/>1.4 Boosting model performance with advanced techniques<br/><br/>1.5 Summary<br/><br/>1.6 Terms from this chapter<br/><br/> <br/><br/>2. Real-world data<br/><br/>2.1 Getting started: data collection<br/><br/>2.2 Preprocessing the data for modeling<br/><br/>2.4 Summary<br/><br/>2.5 Terms from this chapter<br/><br/> <br/><br/>3 Modeling and prediction<br/><br/>3.1 Basic machine-learning modeling<br/><br/>3.2 Classification: predicting into buckets<br/><br/>3.3 Regression: predicting numerical values<br/><br/>3.4 Summary<br/><br/>3.5 Terms from this chapter<br/><br/> <br/><br/>4 Model evaluation and optimization<br/><br/>4.1 Model generalization: assessing predictive accuracy for new data<br/><br/>4.2 Evaluation of classification models<br/><br/>4.3 Evaluation of regression models<br/><br/>4.4 Model optimization through parameter tuning<br/><br/>4.5 Summary<br/><br/>4.6 Terms from this chapter<br/><br/> <br/><br/>5 Basic feature engineering<br/><br/>5.1 Motivation: why is feature engineering useful?<br/><br/>5.2 Basic feature-engineering processes<br/><br/>5.3 Feature selection<br/><br/>5.4 Summary<br/><br/>5.5 Terms from this chapter<br/><br/> <br/><br/>6 Example: NYC taxi data<br/><br/>6.1 Data: NYC taxi trip and fare information<br/><br/>6.2 Modeling<br/><br/>6.3 Summary<br/><br/>6.4 Terms from this chapter<br/><br/> <br/><br/>7 Advanced feature engineering<br/><br/>7.1 Advanced text features<br/><br/>7.2 Image features<br/><br/>7.3 Time-series features<br/><br/>7.4 Summary<br/><br/>7.5 Terms from this chapter<br/><br/> <br/><br/>8 Advanced NLP example: movie review sentiment<br/><br/>8.1 Exploring the data and use case<br/><br/>8.2 Extracting basic NLP features and building the initial model<br/><br/>8.3 Advanced algorithms and model deployment considerations<br/><br/>8.4 Summary<br/><br/>8.5 Terms from this chapter<br/><br/> <br/><br/>9 Scaling machine-learning workflows<br/><br/>9.1 Before scaling up<br/><br/>9.2 Scaling ML modeling pipelines<br/><br/>9.3 Scaling predictions<br/><br/>9.4 Summary<br/><br/>9.5 Terms from this chapter<br/><br/> <br/><br/>10 Example: digital display advertising<br/><br/>10.1 Display advertising<br/><br/>10.2 Digital advertising data<br/><br/>10.3 Feature engineering and modeling strategy<br/><br/>10.4 Size and shape of the data<br/><br/>10.5 Singular value decomposition<br/><br/>10.6 Resource estimation and optimization<br/><br/>10.7 Modeling<br/><br/>10.8 K-nearest neighbors<br/><br/>10.9 Random forests<br/><br/>10.10 Other real-world considerations<br/><br/>10.11 Summary<br/><br/>10.12 Terms from this chapter<br/><br/>10.13 Recap and conclusion
520 ## - SUMMARY, ETC.
Summary, etc.	Machine learning systems help you find valuable insights and patters in data which you had never recognized in the traditional methods. In the real world, ML techniques give you a way to identify trends, forecast behavior and make fact-based recommendations. It’s a hot and growing field and up-to speed ML developers are in demand. Real-World Machine Learning will teach you the concepts and techniques you need to be a successful machine learning practitioner without overdosing you on abstract theory and complex mathematics. By working through immediately relevant examples in Python, you’ll build skills in data acquisition and modelling, classification and regression.
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	Python (Computer program language)
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element	Data mining
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name	Richards, Joseph W.
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name	Fetherolf, Mark
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme	Dewey Decimal Classification
Koha item type	Book

Holdings
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	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.	559.30		006.31 BRI	004875	03/22/2023	1	799.00	03/22/2023	Book