Machine learning with python cookbook : Practical soulutions from preprocessing to deep learning

By:

Albon, Chris

Language: ENG Publication details: Sebastopol O'Reilly Media, Inc. 2018Edition: 1stDescription: xiii, 349p. | Binding - Paperback | 23*17.6 cmISBN:

9789352137305

Subject(s):

DDC classification:

DDC23 006.31 ALB

Summary: 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

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Item type	Current library	Collection	Call number	Status	Date due	Barcode
Books	School of Engineering & Technology Reference Section	Reference	006.31 ALB (Browse shelf(Opens below))	Not For Loan		E14611
Books	School of Engineering & Technology General Stacks	Circulation	006.31 ALB (Browse shelf(Opens below))	Available		E14612
Books	School of Engineering & Technology General Stacks	Circulation	006.31 ALB (Browse shelf(Opens below))	Available		E14613
Books	School of Engineering & Technology General Stacks	Circulation	006.31 ALB (Browse shelf(Opens below))	Checked out to SHAH AZEEM (GB24EC20)	05/05/2026	E14614
Books	School of Engineering & Technology General Stacks	Circulation	006.31 ALB (Browse shelf(Opens below))	Available		E14615

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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

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