000			01992nam a22003375i 4500
001			23494766
003			Bd-DhDIU
005			20250526090813.0
008			240110s2023 caua 001 0 eng
020			_a9781098135720 _qpaperback
020			_a1098135725
035			_a(OCoLC)on1400087878
040			_aUKMGB _beng _erda _cUKMGB _dJRZ _dJOZ _dOCLCO _dJOZ _dOCLCF _dOCLCO _dBd-DhDIU
082	0	4	_a006.31/ G162m _223
100	1		_aGallatin, Kyle, _eauthor. _915706
245	1	0	_aMachine learning with Python cookbook : _bpractical solutions from preprocessing to deep learning / _cKyle Gallatin and Chris Albon.
250			_a2nd ed.
260			_aBoston : _bO'Reilly Media Inc, _c2023.
300			_axiv, 398 pages : _billustrations (black and white) ; _c24 cm
500			_aPrevious ed.: / Chris Albon. 2018.
500			_aIncludes index.
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. _918073
650		0	_aPython (Computer program language)
650		6	_aApprentissage automatique. _915707
650		6	_aPython (Langage de programmation) _915708
650		7	_aMachine learning _2fast
650		7	_aPython (Computer program language) _2fast
700	1		_aAlbon, Chris, _eauthor. _915709
942			_2ddc _cBK _011
999			_c16518 _d16518