book

The Applied AI and Natural Language Processing Workshop

Name: The Applied AI and Natural Language Processing Workshop
ISBN: 9781800208742

by Krishna Sankar, Jeffrey Jackovich, Ruze Richards, Ridhima Garg, Sasikanth Kotti, Ankit Malik, Sagnik Pal, Robert Ridley, Priyanka Singh

July 2020

Intermediate to advanced

384 pages

6h 13m

English

Packt Publishing

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The Applied AI and Natural Language Processing Workshop
Preface
About the BookAudienceAbout the ChaptersConventionsCode PresentationSetting up Your EnvironmentSoftware RequirementsInstallation and SetupAWS AccountA Word about AWS RegionsAWS CLI SetupConfiguration and Credential files for the AWS CLIAmazon Rekognition AccountInstalling Python and AnacondaInstalling Python and Anaconda on WindowsInstalling Python and Anaconda on LinuxInstalling Python and Anaconda on macOSProject JupyterInstalling LibrariesAccessing the Code Files
1. An Introduction to AWS
IntroductionHow Is AWS Special?What Is ML?What Is AI?What Is Amazon S3?Why Use S3?The Basics of Working on AWS with S3AWS Free-Tier AccountAWS Account Setup and NavigationDownloading the Support Materials for This BookA Word about Jupyter NotebooksImporting and Exporting Data into S3How S3 Differs from a FilesystemCore S3 ConceptsS3 OperationsData ReplicationThe REST InterfaceExercise 1.01: Using the AWS Management Console to Create an S3 BucketExercise 1.02: Importing and Exporting the File with Your S3 BucketThe AWS CLIExercise 1.03: Configuring the CLICLI UsageRecursion and ParametersActivity 1.01: Putting the Data into S3 with the CLIUsing the AWS Console to Identify ML ServicesExercise 1.04: Navigating the AWS Management ConsoleExercise 1.05: Testing the Amazon Comprehend API FeaturesThe Utility of the AWS Console Interface to AI ServicesSummary
2. Analyzing Documents and Text with Natural Language Processing
IntroductionServerless ComputingAmazon Lambda and Function as a ServiceServerless Computing as an ApproachAmazon ComprehendWhat Is an NLP Service?Using Amazon Comprehend to Inspect Text and Determine the Primary LanguageExercise 2.01: Detecting the Dominant Language in a Text Document Using the Command-Line InterfaceExercise 2.02: Detecting the Dominant Language in Multiple Documents by Using the CLIExtracting Information from a Set of DocumentsDetecting Named Entities—AWS SDK for Python (boto3)DetectEntities – Input and OutputExercise 2.03: Determining the Named Entities in a Document (the DetectEntities method)Exercise 2.04: Detecting Entities in a Set of Documents (Text Files)Detecting Key PhrasesExercise 2.05: Detecting Key PhrasesDetecting SentimentsExercise 2.06: Conducting Sentiment AnalysisSetting Up a Lambda Function and Analyzing Imported Text Using ComprehendIntegrating Comprehend and AWS Lambda for responsive NLPWhat Is AWS Lambda?What Does AWS Lambda Do?Lambda Function AnatomyExercise 2.07: Setting Up a Lambda Function for S3Exercise 2.08: Assigning Policies to S3_trigger to Access ComprehendActivity 2.01: Integrating Lambda with Amazon Comprehend to Perform Text AnalysisAmazon TextractExercise 2.09: Extracting Tax Information Using Amazon TextractSummary
3. Topic Modeling and Theme Extraction
IntroductionTopic Modeling with Latent Dirichlet Allocation (LDA)Basic LDA ExampleWhy Use LDA?Amazon Comprehend—Topic Modeling GuidelinesExercise 3.01: Using Amazon Comprehend to Perform Topic Modeling on Two Documents with Known TopicsExercise 3.02: Performing Known Structure Analysis ProgrammaticallyActivity 3.01: Performing Topic Modeling on a Set of Documents with Unknown TopicsSummary
4. Conversational Artificial Intelligence
Introduction to Conversational AIInteraction TypesOmnichannelWhat Is a Chatbot?What Is Natural Language Understanding?Core Concepts in a NutshellChatbotUtterancesIntentPromptsSlotFulfillmentBest Practices for Designing Conversational AICreating a Custom ChatbotA Bot That Recognizes an Intent and Filling a SlotExercise 4.01: Creating a Bot That Will Recognize an Intent and Fill a Slot Natural Language Understanding EngineLambda Function – Implementing Business LogicExercise 4.02: Creating a Lambda Function to Handle Chatbot FulfillmentImplementing the Lambda FunctionInput Parameter StructureImplementing the High-Level Handler FunctionImplementing the Function to Retrieve the Market QuoteReturning the Information to the Calling App (the Chatbot)Connecting to the ChatbotDebugging TipsSummary
5. Using Speech with the Chatbot
Amazon Connect BasicsFree Tier InformationInteracting with the ChatbotTalking to Your Chatbot through a Call Center Using Amazon ConnectExercise 5.01: Creating a Personal Call CenterExercise 5.02: Obtaining a Free Phone Number for Your Call CenterUsing Amazon Lex Chatbots with Amazon ConnectUnderstanding Contact FlowsContact Flow TemplatesExercise 5.03: Connecting the Call Center to Your Lex ChatbotActivity 5.01: Creating a Custom Bot and Connecting the Bot with Amazon ConnectSummary
6. Computer Vision and Image Processing
IntroductionAmazon Rekognition BasicsFree Tier Information on Amazon RekognitionRekognition and Deep LearningDetecting Objects and Scenes in ImagesExercise 6.01: Detecting Objects and Scenes Using Your ImagesImage ModerationExercise 6.02: Detecting Objectionable Content in ImagesFacial AnalysisExercise 6.03: Analyzing Faces with Your Own ImagesCelebrity RecognitionExercise 6.04: Recognizing Celebrities in Your ImagesFace ComparisonActivity 6.01: Creating and Analyzing Different Faces in RekognitionText in ImagesExercise 6.05: Extracting Text from Your Own ImagesSummary
Appendix
1. An Introduction to AWSActivity 1.01: Putting the Data into S3 with the CLI2. Analyzing Documents and Text with Natural Language ProcessingActivity 2.01: Integrating Lambda with Amazon Comprehend to Perform Text Analysis3. Topic Modeling and Theme ExtractionActivity 3.01: Performing Topic Modeling on a Set of Documents with Unknown Topics5. Using Speech with the ChatbotActivity 5.01: Creating a Custom Bot and Connecting the Bot with Amazon Connect6. Computer Vision and Image ProcessingActivity 6.01: Creating and Analyzing Different Faces in Rekognition

Overview

Delve into the practical integration of artificial intelligence with Amazon Web Services in 'The Applied AI and Natural Language Processing Workshop'. Throughout this guide, you will learn the methods and skills to create applications such as chatbots, image recognition systems, and more, utilizing AWS services effectively.

What this Book will help me do

Understand the fundamentals of AI, ML, and AWS.
Learn how to use Amazon S3 for data storage and manipulation.
Develop algorithms to analyze and extract themes from documents using NLP.
Create interactive chatbots capable of task automation and API integration.
Master the application of computer vision techniques using Amazon Rekognition.

Author(s)

The book is authored by Krishna Sankar, Jeffrey Jackovich, and Ruze Richards, renowned experts in artificial intelligence and cloud computing. Their combined experience in developing state-of-the-art applications is evident in their practical and approachable teaching style. They aim to empower readers to confidently apply AI and NLP technologies in innovative ways.

Who is it for?

This book is targeted towards machine learning enthusiasts, data scientists, and programmers who are keen on exploring AWS's AI and machine learning capabilities. It's suitable for individuals at the beginner to intermediate level, with a basic understanding of AI being beneficial but not necessary. Whether you're a professional looking to enhance your skills or a hobbyist seeking to innovate, this book offers valuable insights and guidance.

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The Natural Language Processing Workshop

Publisher Resources

ISBN: 9781800208742

Cloud Computing

Data Engineering

Data Science

AI & ML

Programming Languages

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IT/Ops

Security

Design

Business

Soft Skills