9781491989388-1491989386-Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning

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

ISBN-13: 9781491989388
ISBN-10: 1491989386
Edition: 1
Author: Chris Albon
Publication date: 2018
Publisher: O'Reilly Media
Format: Paperback 364 pages
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Book details

ISBN-13: 9781491989388
ISBN-10: 1491989386
Edition: 1
Author: Chris Albon
Publication date: 2018
Publisher: O'Reilly Media
Format: Paperback 364 pages

Summary

Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning (ISBN-13: 9781491989388 and ISBN-10: 1491989386), written by authors Chris Albon, was published by O'Reilly Media in 2018. With an overall rating of 3.6 stars, it's a notable title among other Data Modeling & Design (Databases & Big Data, Data Mining, Data Processing, Algorithms, Programming) books. You can easily purchase or rent Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning (Paperback) from BooksRun, along with many other new and used Data Modeling & Design books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $2.16.

Description

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
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