Gallatin, Kyle, <br/><br/>
Machine learning with Python cookbook :  practical solutions from preprocessing to deep learning / 
Kyle Gallatin and Chris Albon. 
 - Second edition. 
 - xiv, 398 pages :  illustrations (black and white) ;  24 cm 
<br/><br/> Previous ed.: / Chris Albon. 2018.  Includes index. 
<br/><br/>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 
<br/><br/> 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. 
<br/><br/><label>ISBN: </label>9781098135720 1098135725 
<br/><br/><label>LCCN: </label>2023302490 
<br/><br/><label>Nat. Bib. No.: </label>GBC3F6725  bnb 
<br/><br/><label>Nat. Bib. Agency Control No.: </label>021178905  Uk 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Machine learning.<br/>Python (Computer program language)<br/>Apprentissage automatique.<br/>Python (Langage de programmation)<br/>Machine learning<br/>Python (Computer program language)
<br/><br/><label>LC Class. No.: </label>Q325.5  / .A425 2023 
<br/><br/><label>Dewey Class. No.: </label>006.31 GAL  / 24732