Description
This practical guide provides more than 200 self-contained recipes to help you solve machine learning challenges you may encounter in your work. If youre comfortable with Python and its libraries, including pandas and scikit-learn, youll be able to address specific problems, 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 that 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. Youll 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
Supporting vector machines (SVM), nae Bayes, clustering, and tree-based models
Saving, loading, and serving trained models from multiple frameworks
Author: Kyle Gallatin, Chris Albon
Binding Type: Paperback
Publisher: OReilly Media
Published: 09/05/2023
Pages: 413
Weight: 1.45lbs
Size: 9.19h x 7.00w x 0.85d
ISBN: 9781098135720
Language: English
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