book

Machine Learning in the AWS Cloud

by Abhishek Mishra

September 2019

Intermediate to advanced

528 pages

14h 24m

English

Sybex

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Who This Book Is ForWhat This Book CoversHow This Book Is StructuredWhat You Need to Use This BookConventionsSource CodeErrata
What Is Machine Learning?Types of Machine Learning SystemsThe Traditional Versus the Machine Learning ApproachSummary
Machine Learning DatasetsData Preprocessing TechniquesSummary
Introducing MatplotlibComponents of a PlotCommon PlotsSummary
Introducing Scikit-learnCreating a Training and Test DatasetCreating Machine Learning ModelsSummary

Evaluating Regression ModelsEvaluating Classification ModelsChoosing Hyperparameter ValuesSummary
What Is Cloud Computing?Cloud Service ModelsCloud Deployment ModelsThe AWS EcosystemSign Up for an AWS Free-Tier AccountSummaryNote
Regions and Availability ZonesEdge LocationsAccessing AWSSummary
Key ConceptsCommon TasksSummary
Key ConceptsCommon TasksSummary
Key ConceptsCommon TasksUser Pools or Identity Pools: Which One Should You Use?Summary
Key ConceptsCommon TasksSummary
Common Use Cases for LambdaKey ConceptsCommon TasksSummary
Key ConceptsText Analysis Using the Amazon Comprehend Management ConsoleInteractive Text Analysis with the AWS CLIUsing Amazon Comprehend with AWS LambdaSummary
Key ConceptsCreating an Amazon Lex BotSummary
Key ConceptsCreating DatasourcesViewing Data InsightsCreating an ML ModelMaking Batch PredictionsCreating a Real-Time Prediction Endpoint for Your Machine Learning ModelMaking Predictions Using the AWS CLIUsing Real-Time Prediction Endpoints with Your ApplicationsSummary
Key ConceptsCreating an Amazon SageMaker Notebook InstancePreparing Test and Training DataTraining a Scikit-Learn Model on an Amazon SageMaker Notebook InstanceTraining a Scikit-Learn Model on a Dedicated Training InstanceTraining a Model Using a Built-in Algorithm on a Dedicated Training InstanceSummary
Introduction to Google TensorFlowCreating a Linear Regression Model with Google TensorFlowTraining and Deploying a DNN Classifier Using the TensorFlow Estimators API and Amazon SageMakerSummary
Key ConceptsAnalyzing Images Using the Amazon Rekognition Management ConsoleInteractive Image Analysis with the AWS CLIUsing Amazon Rekognition with AWS LambdaSummary
Installing the Anaconda DistributionCreating a Conda Python EnvironmentInstalling Python PackagesInstalling Jupyter NotebookSummary
Creating an IAM User for DevelopmentCreating S3 Buckets
Mac OS UsersWindows Users
NumPyPandas

Content preview from Machine Learning in the AWS Cloud

Chapter 3Data Visualization with Python

WHAT'S IN THIS CHAPTER

Introduction to Matplotlib
Learn to create histograms, bar charts, and pie charts
Learn to create box plots and scatter plots
Learn to use Pandas plotting functions

In the previous chapter, you learned about techniques to explore data and perform feature engineering with NumPy, Pandas, and Scikit-learn. In this chapter you will learn to use Matplotlib to visualize data. Data visualization helps to understand the characteristics and relationships between the features during the data exploration phase but becomes particularly important when you are dealing with very large datasets that have several hundreds of features.

NOTE To follow along with this chapter ensure you have installed ...

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Publisher Resources

ISBN: 9781119556718Purchase book