Machine learning with Python cookbook : practical solutions from preprocessing to deep learning / Kyle Gallatin and Chris Albon.

By:

Gallatin, Kyle [author.]

Contributor(s):

Albon, Chris [author.]

Material type: Text

TextPublisher: Sebastopol, CA : O'Reilly Media Inc, 2023Edition: Second editionDescription: xiv, 398 pages : illustrations (black and white) ; 24 cmContent type:

text

Media type:

unmediated

Carrier type:

volume

ISBN:

9781098135720
1098135725

Subject(s):

DDC classification:

006.31 GAL 23 24732

LOC classification:

Q325.5 .A425 2023

Contents:

Working with vectors, matrices, and arrays in NumPy -- Losding data -- Data wrangling -- Handling numerical data -- Handling categorical data -- Handling text --Handling dates and times -- Handling images --Dimension reduction using feature extraction -- Dimensionality redutctuion using feature selection

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.

Tags from this library: No tags from this library for this title. Log in to add tags.

Average rating: 0.0 (0 votes)

Holdings
Item type	Current library	Call number	Status	Date due	Barcode	Item holds
Books	COMSATS University Wah Campus	006.31 GAL 24732 (Browse shelf(Opens below))	Available		10004000024732

Total holds: 0

Browsing COMSATS University Wah Campus shelves Close shelf browser (Hides shelf browser)

Previous					No cover image available			Next
Previous	006.31 CHE 24721 Reliable machine learning : applying SRE principles to ML in production /	006.31 DRO 24688 The science of deep learning /	006.31 DUT 23613 Machine learning	006.31 GAL 24732 Machine learning with Python cookbook : practical solutions from preprocessing to deep learning /	006.31 GOL Genetic Algorithms In Search, optimization And machine Learning	006.31 HAS The elements of statistical learning data mining, inference, and prediction.	006.31 KEY 24678 Deep learning and scientific computing with R torch	Next

Previous ed.: / Chris Albon. 2018.

Includes index.

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.

There are no comments on this title.

to post a comment.