Python kikai gakushū kukkubukku / Kyle Gallatin, Chris Albon cho ; Nakada Hidemoto yaku = Machine learning with Python cookbook : second edition : practical solutions from preprocessing to deep learning / Kyle Gallatin and Chris Albon.

Format:

Book

Author/Creator:

Albon, Chris, author.

Gallatin, Kyle, author.

Contributor:

Nakada, Hidemoto, translator.

Standardized Title:

Machine learning with Python cookbook. Japanese

Language:

Japanese

Subjects (All):

Machine learning.

Python (Computer program language).

Physical Description:

1 online resource (428 pages)

Edition:

Dai 2-han.

Place of Publication:

Tōkyō-to Shinjuku-ku : Orairī Japan, 2024.

Summary:

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, 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.

Notes:

OCLC-licensed vendor bibliographic record.

ISBN:

9784814400843

4814400845

OCLC:

1457973530