Machine Learning with AWS

Book description

Use artificial intelligence and machine learning on AWS to create engaging applications

Key Features

  • Explore popular AI and ML services with their underlying algorithms
  • Use the AWS environment to manage your AI workflow
  • Reinforce key concepts with hands-on exercises using real-world datasets

Book Description

Machine Learning with AWS is the right place to start if you are a beginner interested in learning useful artificial intelligence (AI) and machine learning skills using Amazon Web Services (AWS), the most popular and powerful cloud platform. You will learn how to use AWS to transform your projects into apps that work at high speed and are highly scalable. From natural language processing (NLP) applications, such as language translation and understanding news articles and other text sources, to creating chatbots with both voice and text interfaces, you will learn all that there is to know about using AWS to your advantage. You will also understand how to process huge numbers of images fast and create machine learning models.

By the end of this book, you will have developed the skills you need to efficiently use AWS in your machine learning and artificial intelligence projects.

What you will learn

  • Get up and running with machine learning on the AWS platform
  • Analyze unstructured text using AI and Amazon Comprehend
  • Create a chatbot and interact with it using speech and text input
  • Retrieve external data via your chatbot
  • Develop a natural language interface
  • Apply AI to images and videos with Amazon Rekognition

Who this book is for

Machine Learning with AWS is ideal for data scientists, programmers, and machine learning enthusiasts who want to learn about the artificial intelligence and machine learning capabilities of Amazon Web Services.

Publisher resources

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Table of contents

  1. Preface
    1. About the Book
      1. About the Authors
      2. Objectives
      3. Audience
      4. Approach
      5. Minimum Hardware Requirements
      6. Software Requirements
      7. Installation and Setup
  2. Introduction to Amazon Web Services
    1. Introduction
    2. What is AWS?
      1. What is Machine Learning?
      2. What is Artificial Intelligence?
    3. What is Amazon S3?
      1. Why use S3?
      2. The Basics of Working on AWS with S3
      3. AWS Free-Tier Account
      4. Importing and Exporting Data into S3
      5. How S3 Differs from a Filesystem
    4. Core S3 Concepts
      1. S3 Operations
    5. Data Replication
      1. REST Interface
      2. Exercise 1: Using the AWS Management Console to Create an S3 Bucket
      3. Exercise 2: Importing and Exporting the File with your S3 Bucket
    6. AWS Command-Line Interface (CLI)
      1. Exercise 3: Configuring the Command-Line Interface
    7. Command Line-Interface (CLI) Usage
    8. Recursion and Parameters
      1. Activity 1: Importing and Exporting the Data into S3 with the CLI
    9. Using the AWS Console to Identify Machine Learning Services
      1. Exercise 4: Navigating the AWS Management Console
      2. Activity 2: Testing the Amazon Comprehend's API Features
    10. Summary
  3. Summarizing Text Documents Using NLP
    1. Introduction
    2. What is Natural Language Processing?
    3. Using Amazon Comprehend to Inspect Text and Determine the Primary Language
      1. Exercise 5: Detecting the Dominant Language Using the Command-Line Interface in a text document
      2. Exercise 6: Detecting the Dominant Language in Multiple Documents by Using the Command-Line Interface (CLI)
    4. Extracting Information in a Set of Documents
      1. Detecting Named Entities – AWS SDK for Python (boto3)
      2. DetectEntites – Input and Output
      3. Exercise 7: Determining the Named Entities in a Document
      4. DetectEntities in a Set of Documents (Text Files)
      5. Detecting Key Phrases
      6. Exercise 8: Determining the Key Phrase Detection.
      7. Detecting Sentiments
      8. Exercise 9: Detecting Sentiment Analysis
    5. Setting up a Lambda function and Analyzing Imported Text Using Comprehend
      1. What is AWS Lambda?
      2. What does AWS Lambda do?
      3. Lambda Function Anatomy
      4. Exercise 10: Setting up a Lambda function for S3
      5. Exercise 11: Configuring the Trigger for an S3 Bucket
      6. Exercise 12: Assigning Policies to S3_trigger to Access Comprehend
      7. Activity 3: Integrating Lambda with Amazon Comprehend to Perform Text Analysis
    6. Summary
  4. Perform Topic Modeling and Theme Extraction
    1. Introduction
    2. Extracting and Analyzing Common Themes
      1. Topic Modeling with Latent Dirichlet Allocation (LDA)
      2. Basic LDA example
      3. Why Use LDA?
      4. Amazon Comprehend–Topic Modeling Guidelines
      5. Exercise 13: Topic Modeling of a Known Topic Structure
      6. Exercise 14: Performing Known Structure Analysis
      7. Activity 4: Perform Topic Modeling on a Set of Documents with Unknown Topics
      8. Summary
  5. Creating a Chatbot with Natural Language
    1. Introduction
    2. What is a Chatbot?
      1. The Business Case for Chatbots
    3. What is Natural Language Understanding?
      1. Core Concepts in a Nutshell
    4. Setting Up with Amazon Lex
      1. Introduction
      2. Exercise 15: Creating a Sample Chatbot to Order Flowers
    5. Creating a Custom Chatbot
    6. A Bot Recognizing an Intent and Filling a Slot
      1. Exercise 16: Creating a Bot that will Recognize an Intent and Fill a Slot
      2. Natural Language Understanding Engine
    7. Lambda Function – Implementation of Business Logic
      1. Exercise 17: Creating a Lambda Function to Handle Chatbot Fulfillment
      2. Implementing the Lambda Function
      3. Input Parameter Structure
      4. Implementing the High-Level Handler Function
      5. Implementing the Function to Retrieve the Market Quote
      6. Returning the Information to the Calling App (The Chatbot)
      7. Connecting to the Chatbot
      8. Activity 5: Creating a Custom Bot and Configuring the Bot
    8. Summary
  6. Using Speech with the Chatbot
    1. Introduction
    2. Amazon Connect Basics
      1. Free Tier Information
    3. Interacting with the Chatbot
    4. Talking to Your Chatbot through a Call Center using Amazon Connect
      1. Exercise 18: Creating a Personal Call Center
      2. Exercise 19: Obtaining a Free Phone Number for your Call Center
    5. Using Amazon Lex Chatbots with Amazon Connect
      1. Understanding Contact Flows
      2. Contact Flow Templates
      3. Exercise 20: Connect the Call Center to Your Lex Chatbot
      4. Activity 1: Creating a Custom Bot and Connecting the Bot with Amazon Connect
    6. Summary
  7. Analyzing Images with Computer Vision
    1. Introduction
    2. Amazon Rekognition Basics
      1. Free Tier Information on Amazon Rekognition
    3. Rekognition and Deep Learning
      1. Detect Objects and Scenes in Images
      2. Exercise 21: Detecting Objects and Scenes using your own images
      3. Image Moderation
      4. Exercise 22: Detecting objectionable content in images
      5. Facial Analysis
      6. Exercise 23: Analyzing Faces in your Own Images
      7. Celebrity Recognition
      8. Exercise 24: Recognizing Celebrities in your Own Images
      9. Face Comparison
      10. Activity 1: Creating and Analyzing Different Faces in Rekognition
      11. Text in Images
      12. Exercise 25: Extracting Text from your Own Images
    4. Summary
  8. Appendix A
    1. Chapter 1: Introduction to Amazon Web Services
      1. Activity 1: Importing and exporting the data into S3 with the CLI.
      2. Activity 2: Test Amazon Comprehends API features.
    2. Chapter 2: Summarizing Text Documents Using NLP
      1. Activity 3: Integrating Lambda with Amazon Comprehend to perform text analysis
    3. Chapter 3: Perform Topic Modeling and Theme Extraction
      1. Activity 4: Perform Topic modeling on a set of documents with unknown topics
    4. Chapter 4: Creating a Chatbot with Natural Language
      1. Activity 5: Creating a custom bot and configure the bot
    5. Chapter 5: Using Speech with the Chatbot
      1. Activity 6: Creating a custom bot and connect the bot with Amazon Connect
    6. Chapter 6: Analyzing Images with Computer Vision
      1. Activity 7: Compare faces in your own images

Product information

  • Title: Machine Learning with AWS
  • Author(s): Jeffrey Jackovich, Ruze Richards
  • Release date: November 2018
  • Publisher(s): Packt Publishing
  • ISBN: 9781789806199