An edition of Python machine learning (2015)

Python machine learning

unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics

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Last edited by ImportBot
December 20, 2023 | History
An edition of Python machine learning (2015)

Python machine learning

unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics

  • 0 Ratings
  • 28 Want to read
  • 1 Currently reading
  • 1 Have read

Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data -- its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Pylearn2, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization.

Publish Date
Publisher
Packt Publishing
Language
English
Pages
425

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Previews available in: English

Book Details


Table of Contents

Giving computers the ability to learn from data
Training machine learning algorithms for classification
A tour of machine learning classifiers using Scikit-learn
Building good training sets : data preprocessing
Compressing data via dimensionality reduction
Learning best practices for model evaluation and hyperparameter tuning
Combining different models for ensemble learning
Applying machine learning to sentiment analysis
Embedding a machine learning model into a web application
Predicting continuous target variables with regression analysis
Working with unlabeled data : clustering analysis
Training artificial neural networks for image recognition
Parallelizing neural network training with Theano.

Edition Notes

Includes index.

Series
Community experience distilled, Community experience distilled
Copyright Date
2016

Classifications

Dewey Decimal Class
005.133, 005.133

The Physical Object

Pagination
xiii, 425 pages
Number of pages
425

ID Numbers

Open Library
OL26886340M
Internet Archive
pythonmachinelea0000rasc
ISBN 10
1783555130
ISBN 13
9781783555130
OCLC/WorldCat
922562066
Amazon ID (ASIN)
B00YSILNL0

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History

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December 20, 2023 Edited by ImportBot import existing book
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August 17, 2022 Edited by ImportBot import existing book
May 15, 2019 Created by MARC Bot Imported from marc_openlibraries_phillipsacademy MARC record