Mastering machine learning on AWS: advanced machine learning in Python using SageMaker, Apache Spark, and TensorFlow/ Saket S.R. Mengle, Maximo Gurmendez.

Mengle  Saket S. R.,; Gurmendez Maximo

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dc.contributor.author	Mengle Saket S. R.,
dc.contributor.author	Gurmendez Maximo
dc.date.accessioned	2024-01-26T21:31:48Z
dc.date.available	2024-01-26T21:31:48Z
dc.date.issued	2019
dc.identifier.citation	Mengle. Mastering machine learning on AWS: advanced machine learning in Python using SageMaker, Apache Spark, and TensorFlow - 1 online resource (293 pages) - URL: https://libweb.kpfu.ru/ebsco/pdf/2142587.pdf
dc.identifier.isbn	1789347505
dc.identifier.isbn	9781789347500
dc.identifier.uri	https://dspace.kpfu.ru/xmlui/handle/net/178219
dc.description.abstract	This book will help you master your skills in various artificial intelligence and machine learning services available on AWS. Through practical hands-on examples, you'll learn how to use these services to generate impressive results. You will have a tremendous understanding of how to use a wide range of AWS services in your own organization.
dc.description.tableofcontents	Cover; Title Page; Copyright and Credits; Dedication; About Packt; Contributors; Table of Contents; Preface; Section 1: Machine Learning on AWS; Chapter 1: Getting Started with Machine Learning for AWS; How AWS empowers data scientists; Using AWS tools for machine learning; Identifying candidate problems that can be solved using machine learning; Machine learning project life cycle; Data gathering; Evaluation metrics; Algorithm selection; Deploying models; Summary; Exercise; Section 2: Implementing Machine Learning Algorithms at Scale on AWS
dc.description.tableofcontents	Chapter 2: Classifying Twitter Feeds with Naive BayesClassification algorithms; Feature types; Nominal features; Ordinal features; Continuous features; Naive Bayes classifier; Bayes' theorem; Posterior; Likelihood; Prior probability; Evidence; How the Naive Bayes algorithm works; Classifying text with language models; Collecting the tweets; Preparing the data; Building a Naive Bayes model through SageMaker notebooks; Naïve Bayes model on SageMaker notebooks using Apache Spark; Using SageMaker's BlazingText built-in ML service; Naive Bayes - pros and cons; Summary; Exercises
dc.description.tableofcontents	Chapter 3: Predicting House Value with Regression AlgorithmsPredicting the price of houses; Understanding linear regression; Linear least squares estimation; Maximum likelihood estimation; Gradient descent; Evaluating regression models; Mean absolute error; Mean squared error; Root mean squared error; R-squared; Implementing linear regression through scikit-learn; Implementing linear regression through Apache Spark; Implementing linear regression through SageMaker's linear Learner; Understanding logistic regression; Logistic regression in Spark; Pros and cons of linear models; Summary
dc.description.tableofcontents	Chapter 4: Predicting User Behavior with Tree-Based MethodsUnderstanding decision trees; Recursive splitting; Types of decision trees; Cost functions; Gini Impurity; Information gain; Criteria to stop splitting trees; Understanding random forest algorithms; Understanding gradient boosting algorithms; Predicting clicks on log streams; Introduction to Elastic Map Reduce (EMR); Training with Apache Spark on EMR; Getting the data; Preparing the data; Categorical encoding; One-hot encoding; Training a model; Evaluating our model; Area Under ROC Curve; Area under the precision-recall curve; Training tree ensembles on EMR Training gradient-boosted trees with the SageMaker services; Preparing the data; Training with SageMaker XGBoost; Applying and evaluating the model; Summary; Exercises
dc.description.tableofcontents	Chapter 5: Customer Segmentation Using Clustering Algorithms; Understanding How Clustering Algorithms Work; k-means clustering; Euclidean distance; Manhattan distance; Hierarchical clustering; Agglomerative clustering; Divisive clustering; Clustering with Apache Spark on EMR; Clustering with Spark and SageMaker on EMR; Understanding the purpose of the IAM role; Summary; Exercises; Chapter 6: Analyzing Visitor Patterns to Make Recommendations
dc.language	English
dc.language.iso	en
dc.subject.other	Machine learning.
dc.subject.other	Python (Computer program language)
dc.subject.other	Data mining.
dc.subject.other	COMPUTERS / General.
dc.subject.other	Electronic books.
dc.title	Mastering machine learning on AWS: advanced machine learning in Python using SageMaker, Apache Spark, and TensorFlow/ Saket S.R. Mengle, Maximo Gurmendez.
dc.title.alternative	Mastering machine learning on Amazon Web Services
dc.type	Book
dc.description.pages	1 online resource (293 pages)
dc.collection	Электронно-библиотечные системы
dc.source.id	EN05CEBSCO05C30308