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020 _a9781098135720
_qpaperback
020 _a1098135725
035 _a(OCoLC)on1400087878
040 _aUKMGB
_beng
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050 0 0 _aQ325.5
_b.A425 2023
082 0 4 _a006.31 GAL
_223
_b24732
100 1 _aGallatin, Kyle,
_eauthor.
245 1 0 _aMachine learning with Python cookbook :
_bpractical solutions from preprocessing to deep learning /
_cKyle Gallatin and Chris Albon.
250 _aSecond edition.
264 1 _aSebastopol, CA :
_bO'Reilly Media Inc,
_c2023.
300 _axiv, 398 pages :
_billustrations (black and white) ;
_c24 cm
336 _atext
_btxt
_2rdacontent
337 _aunmediated
_bn
_2rdamedia
338 _avolume
_bnc
_2rdacarrier
500 _aPrevious ed.: / Chris Albon. 2018.
500 _aIncludes index.
505 0 _aWorking 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
520 _aThis 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.
650 0 _aMachine learning.
650 0 _aPython (Computer program language)
650 6 _aApprentissage automatique.
650 6 _aPython (Langage de programmation)
650 7 _aMachine learning
_2fast
650 7 _aPython (Computer program language)
_2fast
700 1 _aAlbon, Chris,
_eauthor.
852 _p10004000024732
906 _a7
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