Mishra, Abhishek.
Machine learning in the aws cloud add intelligence to applications with amazon sagemaker and amazon rekognition / [electronic resource] : Abhishek Mishra. - San Francisco, Calif. : Sybex, 2019. - 1 online resource
Front 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.
Put 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.
9781119556725 1119556724 9781119556749 1119556740 9781119556732 1119556732
EB00764700 Recorded Books
327B574E-1719-44E0-A7E1-C0F710363365 OverDrive, Inc. http://www.overdrive.com
Amazon Web Services (Firm)
Amazon Web Services (Firm)
Machine learning.
Cloud computing.
COMPUTERS / Machine Theory.
Cloud computing.
Machine learning.
Electronic books.
Q325.5
006.3/1
Machine learning in the aws cloud add intelligence to applications with amazon sagemaker and amazon rekognition / [electronic resource] : Abhishek Mishra. - San Francisco, Calif. : Sybex, 2019. - 1 online resource
Front 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.
Put 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.
9781119556725 1119556724 9781119556749 1119556740 9781119556732 1119556732
EB00764700 Recorded Books
327B574E-1719-44E0-A7E1-C0F710363365 OverDrive, Inc. http://www.overdrive.com
Amazon Web Services (Firm)
Amazon Web Services (Firm)
Machine learning.
Cloud computing.
COMPUTERS / Machine Theory.
Cloud computing.
Machine learning.
Electronic books.
Q325.5
006.3/1