Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning
This practical guide provides more than 200 self-contained recipes to help you solve machine learning challenges you may encounter in your work. If you're comfortable with Python and its libraries, including pandas and scikit-learn, you'll be able to address specific problems all the way from loading data to training models and leveraging neural networks.

Each recipe in this updated edition includes code that you can copy, paste, and run with a toy dataset to ensure it works. From there, you can adapt these recipes according to your use case or application. Recipes include a discussion that explains the solution and provides meaningful context. Go beyond theory and concepts by learning the nuts and bolts you need to construct working machine learning applications.

You'll find recipes for:

  • Vectors, matrices, and arrays
  • Working with data from CSV, JSON, SQL, databases, cloud storage, and other sources
  • 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), naive Bayes, clustering, and tree-based models
  • Saving and loading trained models from multiple frameworks
1143284608
Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning
This practical guide provides more than 200 self-contained recipes to help you solve machine learning challenges you may encounter in your work. If you're comfortable with Python and its libraries, including pandas and scikit-learn, you'll be able to address specific problems all the way from loading data to training models and leveraging neural networks.

Each recipe in this updated edition includes code that you can copy, paste, and run with a toy dataset to ensure it works. From there, you can adapt these recipes according to your use case or application. Recipes include a discussion that explains the solution and provides meaningful context. Go beyond theory and concepts by learning the nuts and bolts you need to construct working machine learning applications.

You'll find recipes for:

  • Vectors, matrices, and arrays
  • Working with data from CSV, JSON, SQL, databases, cloud storage, and other sources
  • 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), naive Bayes, clustering, and tree-based models
  • Saving and loading trained models from multiple frameworks
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Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning

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

by Kyle Gallatin, Chris Albon
Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning
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Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning

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

by Kyle Gallatin, Chris Albon

Overview

This practical guide provides more than 200 self-contained recipes to help you solve machine learning challenges you may encounter in your work. If you're comfortable with Python and its libraries, including pandas and scikit-learn, you'll be able to address specific problems all the way from loading data to training models and leveraging neural networks.

Each recipe in this updated edition includes code that you can copy, paste, and run with a toy dataset to ensure it works. From there, you can adapt these recipes according to your use case or application. Recipes include a discussion that explains the solution and provides meaningful context. Go beyond theory and concepts by learning the nuts and bolts you need to construct working machine learning applications.

You'll find recipes for:

  • Vectors, matrices, and arrays
  • Working with data from CSV, JSON, SQL, databases, cloud storage, and other sources
  • 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), naive Bayes, clustering, and tree-based models
  • Saving and loading trained models from multiple frameworks

Product Details

ISBN-13: 9781098135720
Publisher: O'Reilly Media Inc
Publication date: 09/12/2023
Edition description: 2nd ed.
Pages: 410
Product dimensions: 7.00(w) x 9.19(h) x 0.85(d)

About the Author

Kyle Gallatin is a software engineer for machine learning infrastructure with years of experience as a data analyst, data scientist and machine learning engineer. He is also a professional data science mentor, volunteer computer science teacher and frequently publishes articles at the intersection of software engineering and machine learning. Currently, Kyle is a software engineer on the machine learning platform team at Etsy.

Chris Albon is the Director of Machine Learning at the Wikimedia Foundation, the non-profit that hosts Wikipedia.
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