Machine Learning With Python Cookbook: Practical Solutions from Preprocessing to Deep Learning

Albon, Chris:

ISBN 10: 1491989386 / ISBN 13: 9781491989388

Published by O'Reilly Media, 2018

About this Item

349 S. - Wir versenden aus unserem deutschen Lager heraus in plastikfreien oder wiederverwendeten Polstertaschen. Sprache: Englisch Gewicht in Gramm: 666 Taschenbuch, Maße: 17.15 cm x 1.91 cm x 22.86 cm.

Seller Inventory # 48290

Bibliographic Details

• Title: Machine Learning With Python Cookbook: Practical Solutions from Preprocessing to Deep Learning
• Publisher: O'Reilly Media
• Publication Date: 2018
• Language: English
• ISBN 10: 1491989386
• ISBN 13: 9781491989388
• Binding: Soft cover
• Condition: Gut
• Seller catalogs:
  • () Ohne Sachgebiet

About this title

Synopsis

This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you’re comfortable with Python and its libraries, including pandas and scikit-learn, you’ll be able to address specific problems such as loading data, handling text or numerical data, model selection, and dimensionality reduction and many other topics.

Each recipe includes code that you can copy and paste into a toy dataset to ensure that it actually works. From there, you can insert, combine, or adapt the code to help construct your application. Recipes also include a discussion that explains the solution and provides meaningful context. This cookbook takes you beyond theory and concepts by providing the nuts and bolts you need to construct working machine learning applications.

You’ll find recipes for:

• Vectors, matrices, and arrays
• Handling numerical and categorical data, text, images, and dates and times
• Dimensionality reduction using feature extraction or feature selection
• Model evaluation and selection
• Linear and logical regression, trees and forests, and k-nearest neighbors
• Support vector machines (SVM), naïve Bayes, clustering, and neural networks
• Saving and loading trained models

About the Author

Chris Albon is data scientist with a Ph.D. in quantitative political science and a decade of experience working in statistical learning, artificial intelligence, and software engineering. He founded New Knowledge, an artificial intelligence company, and previously worked for the crisis and humanitarian non-profit, Ushahidi. Chris also founded and co-hosts of the data science podcast, Partially Derivative.