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Machine learning with Python cookbook : Practical solutions from preprocessing to deep learning

By: Material type: TextTextPublication details: Mumbai: O'reilly: Shhroff Publishers and Distributors, c.2018.Description: xiii+349p. : illustrations ; 24 cmISBN:
  • 978-93-5213-730-5
Subject(s): DDC classification:
  • 005.133 ALB-M
Contents:
Vectors, matrices, and arrays -- Loading data -- Data wrangling -- Handling numerical data -- Handling categorical data -- Handling text -- Handling dates and times -- Handling images -- Dimensionality reduction using feature extraction -- Dimensionality reduction using feature selection -- Model evaluation -- Model selection -- Linear regression -- Trees and forests -- K-nearest neighbors -- Logistic regression -- Support vector machines -- Naive Bayes -- Clustering -- Neural networks -- Saving and loading trained models.
Summary: With Early Release ebooks, you get books in their earliest form--the author's raw and unedited content as he or she writes--so you can take advantage of these technologies long before the official release of these titles. You'll also receive updates when significant changes are made, new chapters are available, and the final ebook bundle is released. The Python programming language and its libraries, including pandas and scikit-learn, provide a production-grade environment to help you accomplish a broad range of machine-learning tasks. With this comprehensive cookbook, data scientists and software engineers familiar with Python will benefit
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Holdings
Item type Current library Home library Call number Status Date due Barcode
Book Book Dept. of Computational Biology and Bioinformatics Processing Center Dept. of Computational Biology and Bioinformatics 005.133 ALB-M (Browse shelf(Opens below)) Checked out to ANU SASI (DCBSMC17001) 04/01/2024 DCB3858

This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work

Vectors, matrices, and arrays --
Loading data --
Data wrangling --
Handling numerical data --
Handling categorical data --
Handling text --
Handling dates and times --
Handling images --
Dimensionality reduction using feature extraction --
Dimensionality reduction using feature selection --
Model evaluation --
Model selection --
Linear regression --
Trees and forests --
K-nearest neighbors --
Logistic regression --
Support vector machines --
Naive Bayes --
Clustering --
Neural networks --
Saving and loading trained models.

With Early Release ebooks, you get books in their earliest form--the author's raw and unedited content as he or she writes--so you can take advantage of these technologies long before the official release of these titles. You'll also receive updates when significant changes are made, new chapters are available, and the final ebook bundle is released. The Python programming language and its libraries, including pandas and scikit-learn, provide a production-grade environment to help you accomplish a broad range of machine-learning tasks. With this comprehensive cookbook, data scientists and software engineers familiar with Python will benefit

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