Machine Learning Pocket Reference: Working With Structured Data In Python

By:

Harrison, Matt

Material type: Text

TextLanguage: English Publication details: Mumbai : Shroff Publishers & Distrib, 2019Description: 303ISBN:

9789352138999

Subject(s):

DDC classification:

006.31 HAR

Summary: 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. 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. This pocket reference includes sections that cover: Classification, using the Titanic dataset Cleaning data and dealing with missing data Exploratory data analysis Common preprocessing steps using sample data Selecting features useful to the model Model selection

List(s) this item appears in: New Arrivals for the Month of March 2023 - Computer Science and Data Science | New Arrivals for the Month of August - 2023

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Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode	Item holds
Book	Alliance College of Engineering and Design	CSE & IT	006.31 HAR (Browse shelf(Opens below))	Available		E11982
Book	Alliance School of Liberal Arts	CSE & IT	006.31 HAR (Browse shelf(Opens below))	Available		LA01302

Total holds: 0

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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.

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.

This pocket reference includes sections that cover:

Classification, using the Titanic dataset
Cleaning data and dealing with missing data
Exploratory data analysis
Common preprocessing steps using sample data
Selecting features useful to the model
Model selection

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