000			04148cam a2200505Ia 4500
001			on1117320705
003			OCoLC
005			20220711203531.0
006			m o d
007			cr cnu---unuuu
008			090713s2019 cau o 000 0 eng d
040			_aRECBK _beng _cRECBK _dOCLCO _dTEFOD _dDG1 _dOCLCF _dUKAHL
020			_a9781119556725 _q(electronic bk.)
020			_a1119556724 _q(electronic bk.)
020			_a9781119556749 _q(electronic bk. : oBook)
020			_a1119556740 _q(electronic bk. : oBook)
020			_a9781119556732 _q(electronic bk.)
020			_a1119556732 _q(electronic bk.)
028	0	2	_aEB00764700 _bRecorded Books
029	1		_aAU@ _b000066121182
035			_a(OCoLC)1117320705
037			_a327B574E-1719-44E0-A7E1-C0F710363365 _bOverDrive, Inc. _nhttp://www.overdrive.com
050		4	_aQ325.5
082	0	4	_a006.3/1 _223
049			_aMAIN
100	1		_aMishra, Abhishek. _98560
245	1	0	_aMachine learning in the aws cloud _h[electronic resource] : _badd intelligence to applications with amazon sagemaker and amazon rekognition / _cAbhishek Mishra.
260			_aSan Francisco, Calif. : _bSybex, _c2019.
300			_a1 online resource
520			_aPut the power of AWS Cloud machine learning services to work in your business and commercial applications! Machine Learning in the AWS Cloud introduces readers to the machine learning (ML) capabilities of the Amazon Web Services ecosystem and provides practical examples to solve real-world regression and classification problems. While readers do not need prior ML experience, they are expected to have some knowledge of Python and a basic knowledge of Amazon Web Services. Part One introduces readers to fundamental machine learning concepts. You will learn about the types of ML systems, how they are used, and challenges you may face with ML solutions. Part Two focuses on machine learning services provided by Amazon Web Services. You'll be introduced to the basics of cloud computing and AWS offerings in the cloud-based machine learning space. Then you'll learn to use Amazon Machine Learning to solve a simpler class of machine learning problems, and Amazon SageMaker to solve more complex problems. - Learn techniques that allow you to preprocess data, basic feature engineering, visualizing data, and model building - Discover common neural network frameworks with Amazon SageMaker - Solve computer vision problems with Amazon Rekognition - Benefit from illustrations, source code examples, and sidebars in each chapter The book appeals to both Python developers and technical/solution architects. Developers will find concrete examples that show them how to perform common ML tasks with Python on AWS. Technical/solution architects will find useful information on the machine learning capabilities of the AWS ecosystem.
505	0		_aFront Matter -- Fundamentals of Machine Learning. Introduction to Machine Learning -- Data Collection and Preprocessing -- Data Visualization with Python -- Creating Machine Learning Models with Scikit-learn -- Evaluating Machine Learning Models -- Machine Learning with Amazon Web Services. Introduction to Amazon Web Services -- AWS Global Infrastructure -- Identity and Access Management -- Amazon S3 -- Amazon Cognito -- Amazon DynamoDB -- AWS Lambda -- Amazon Comprehend -- Amazon Lex -- Amazon Machine Learning -- Amazon SageMaker -- Using Google TensorFlow with Amazon SageMaker -- Amazon Rekognition.
588			_aTitle from resource description page (Recorded Books, viewed September 02, 2019).
610	2	0	_aAmazon Web Services (Firm) _95825
610	2	7	_aAmazon Web Services (Firm) _2fast _0(OCoLC)fst01974501 _95825
650		0	_aMachine learning. _91831
650		0	_aCloud computing. _94659
650		7	_aCOMPUTERS / Machine Theory. _2bisacsh _98561
650		7	_aCloud computing. _2fast _0(OCoLC)fst01745899 _94659
650		7	_aMachine learning. _2fast _0(OCoLC)fst01004795 _91831
655		4	_aElectronic books. _93294
710	2		_aRecorded Books, Inc. _98562
856	4	0	_uhttps://doi.org/10.1002/9781119556749 _zWiley Online Library
942			_cEBK
994			_a92 _bDG1
999			_c69133 _d69133