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  ▼a  1789347505
  ▼q  (ebook)

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  ▼a  9781789347500
  ▼q  (electronic bk.)

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  ▼z  9781789349795

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  ▼a  2142587
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  ▼a  (OCoLC)1102472595

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• 100

  ▼a  Mengle, Saket S. R.,
  ▼e  author.

• 245

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

• 246

  ▼a  Mastering machine learning on Amazon Web Services

• 260

  ▼a  Birmingham, UK:
  ▼b  Packt Publishing, Limited,
  ▼c  2019.

• 300

  ▼a  1 online resource (293 pages).

• 336

  ▼a  text
  ▼b  txt
  ▼2  rdacontent

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  ▼a  computer
  ▼b  c
  ▼2  rdamedia

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  ▼a  online resource
  ▼b  cr
  ▼2  rdacarrier

• 505

  ▼a  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

• 505

  ▼a  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

• 505

  ▼a  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

• 505

  ▼a  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

• 505

  ▼a  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

• 520

  ▼a  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.

• 588

  ▼a  Description based on print version record.

• 590

  ▼a  Master record variable field(s) change: 072, 082

• 650

  ▼a  Machine learning.

• 650

  ▼a  Python (Computer program language)

• 650

  ▼a  Data mining.

• 650

  ▼a  COMPUTERS / General.
  ▼2  bisacsh

• 655

  ▼a  Electronic books.

• 700

  ▼a  Gurmendez, Maximo,
  ▼e  author.

• 776

  ▼i  Print version:
  ▼a  Mengle, Saket S. R.
  ▼t  Mastering machine learning on AWS : advanced machine learning in Python Using SageMaker, Apache Spark, and TensorFlow.
  ▼d  Birmingham : Packt Publishing, Limited, ©2019,
  ▼z  9781789349795

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  ▼3  EBSCOhost
  ▼u  http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2142587

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  ▼b  EBLB
  ▼n  EBL5778831

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  ▼a  EBSCOhost
  ▼b  EBSC
  ▼n  2142587

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  ▼a  강리원

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  ▼a  eBook

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  ▼a  92
  ▼b  N$T