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Machine Learning Pocket Reference: Working With Structured Data In Python

By: Material type: TextTextLanguage: 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 Book Alliance College of Engineering and Design CSE & IT 006.31 HAR (Browse shelf(Opens below)) Available E11982
Book Book Alliance School of Liberal Arts CSE & IT 006.31 HAR (Browse shelf(Opens below)) Available LA01302
Total holds: 0

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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