000			03329nam a22004457i 4500
001			45959
003			BD-DhAAL
005			20260621160624.0
008			260621t20182018caua e 001 0 eng d
010			_a 2018276483
020			_a1491963042
020			_a9781491963043
035			_a21019700
035			_a(OCoLC)962257016
040			_aYDX _beng _cYDX _erda _dOCLCQ _dBTCTA _dGK8 _dSINLB _dJRZ _dBDX _dUUM _dGP5 _dOCLCF _dCLE _dFIE _dCOD _dOCLCQ _dDLC _dBD-DhAAL
042			_alccopycat
050	0	0	_aQA76.73.P98 _bB454 2018
082	0	4	_a006.35 _223
100	1		_aBengfort, Benjamin, _d1984- _eauthor. _961437
245	1	0	_aApplied text analysis with Python : _benabling language-aware data products with machine learning / _cBenjamin Bengfort, Rebecca Bilbro, and Tony Ojeda.
250			_aFirst edition.
260			_aSebastopol, CA : _bO'Reilly Media, Inc., _c2018.
264		4	_c©2018
300			_axviii, 310 pages : _billustrations ; _c25 cm
500			_aIncludes index.
505	0	0	_tLanguage and computation -- _tBuilding a custom corpus -- _tCorpus preprocessing and wrangling -- _tText vectorization and transformation pipelines -- _tClassification for text analysis -- _tClustering for text similarity -- _tContext-aware text analysis -- _tText visualization -- _tGraph analysis of text -- _tChatbots -- _tScaling text analytics with multiprocessing and Spark -- _tDeep learning and beyond.
520			_aFrom news and speeches to informal chatter on social media, natural language is one of the richest and most underutilized sources of data. Not only does it come in a constant stream, always changing and adapting in context; it also contains information that is not conveyed by traditional data sources. The key to unlocking natural language is through the creative application of text analytics. This practical book presents a data scientist's approach to building language-aware products with applied machine learning. You will learn robust, repeatable, and scalable techniques for text analysis with Python, including contextual and linguistic feature engineering, vectorization, classification, topic modeling, entity resolution, graph analysis, and visual steering. By the end of the book, you'll be equipped with practical methods to solve any number of complex real-world problems.- Preprocess and vectorize text into high-dimensional feature representations - Perform document classification and topic modeling - Steer the model selection process with visual diagnostics - Extract key phrases, named entities, and graph structures to reason about data in text - Build a dialog framework to enable chatbots and language-driven interaction - Use Spark to scale processing power and neural networks to scale model complexity.--
526	8		_aAAL
650		0	_aNatural language processing (Computer science)
650		0	_aPython (Computer program language)
650		0	_aMachine learning.
650		7	_aMachine learning.
650		7	_aNatural language processing (Computer science)
650		7	_aPython (Computer program language)
700	1		_aBilbro, Rebecca, _eauthor. _961438
700	1		_aOjeda, Tony, _eauthor. _961439
852	1		_aAyesha Abed Library _cGeneral Stacks
942			_2ddc _cBK
999			_c48151 _d48151