Machine Learning Pocket Reference: Working With Structured Data In Python (Record no. 43258)
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000 -LEADER | |
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fixed length control field | 01470nam a2200205Ia 4500 |
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
fixed length control field | 230309s9999 xx 000 0 und d |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
International Standard Book Number | 9789352138999 |
041 ## - LANGUAGE CODE | |
Language code of text/sound track or separate title | eng |
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
Classification number | 006.31 HAR |
100 ## - MAIN ENTRY--PERSONAL NAME | |
Personal name | Harrison, Matt |
245 ## - TITLE STATEMENT | |
Title | Machine Learning Pocket Reference: Working With Structured Data In Python |
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
Name of publisher, distributor, etc | Shroff Publishers & Distrib |
Place of publication, distribution, etc | Mumbai |
Date of publication, distribution, etc | 2019 |
300 ## - PHYSICAL DESCRIPTION | |
Extent | 303 |
520 ## - SUMMARY, ETC. | |
Summary, etc | With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project.<br/><br/>Ideal for programmers, data scientists, and AI engineers, this book includes an overview of the machine learning process and walks you through classification with structured data. You’ll also learn methods for clustering, predicting a continuous value (regression), and reducing dimensionality, among other topics.<br/><br/>This pocket reference includes sections that cover:<br/><br/>Classification, using the Titanic dataset<br/>Cleaning data and dealing with missing data<br/>Exploratory data analysis<br/>Common preprocessing steps using sample data<br/>Selecting features useful to the model<br/>Model selection |
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name as entry element | Data Science |
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 | structured data |
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name as entry element | Scikit-learn pipeline |
942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
Source of classification or shelving scheme | Dewey Decimal Classification |
Koha item type | Book |
No items available.