Book Image

The Applied AI and Natural Language Processing Workshop

By : Krishna Sankar, Jeffrey Jackovich, Ruze Richards
Book Image

The Applied AI and Natural Language Processing Workshop

By: Krishna Sankar, Jeffrey Jackovich, Ruze Richards

Overview of this book

Are you fascinated with applications like Alexa and Siri and how they accurately process information within seconds before returning accurate results? Are you looking for a practical guide that will teach you how to build intelligent applications that can revolutionize the world of artificial intelligence? The Applied AI and NLP Workshop will take you on a practical journey where you will learn how to build artificial intelligence (AI) and natural language processing (NLP) applications with Amazon Web services (AWS). Starting with an introduction to AI and machine learning, this book will explain how Amazon S3, or Amazon Simple Storage Service, works. You’ll then integrate AI with AWS to build serverless services and use Amazon’s NLP service Comprehend to perform text analysis on a document. As you advance, the book will help you get to grips with topic modeling to extract and analyze common themes on a set of documents with unknown topics. You’ll also work with Amazon Lex to create and customize a chatbot for task automation and use Amazon Rekognition for detecting objects, scenes, and text in images. By the end of The Applied AI and NLP Workshop, you’ll be equipped with the knowledge and skills needed to build scalable intelligent applications with AWS.

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Table of Contents (8 chapters)
Preface
About the Book
1
1. An Introduction to AWS
1. An Introduction to AWS
Introduction
How Is AWS Special?
What Is Amazon S3?
Core S3 Concepts
Data Replication
The AWS CLI
CLI Usage
Recursion and Parameters
Using the AWS Console to Identify ML Services
Summary
Free Chapter
2
2. Analyzing Documents and Text with Natural Language Processing
2. Analyzing Documents and Text with Natural Language Processing
Introduction
Serverless Computing
Amazon Comprehend
What Is an NLP Service?
Using Amazon Comprehend to Inspect Text and Determine the Primary Language
Extracting Information from a Set of Documents
Setting Up a Lambda Function and Analyzing Imported Text Using Comprehend
Amazon Textract
Summary
3
3. Topic Modeling and Theme Extraction
3. Topic Modeling and Theme Extraction
Introduction
Topic Modeling with Latent Dirichlet Allocation (LDA)
Amazon Comprehend—Topic Modeling Guidelines
Summary
4
4. Conversational Artificial Intelligence
4. Conversational Artificial Intelligence
Introduction to Conversational AI
What Is a Chatbot?
What Is Natural Language Understanding?
Best Practices for Designing Conversational AI
Creating a Custom Chatbot
A Bot That Recognizes an Intent and Filling a Slot
Lambda Function – Implementing Business Logic
Summary
5
5. Using Speech with the Chatbot
5. Using Speech with the Chatbot
Amazon Connect Basics
Interacting with the Chatbot
Talking to Your Chatbot through a Call Center Using Amazon Connect
Using Amazon Lex Chatbots with Amazon Connect
Summary
6
6. Computer Vision and Image Processing
6. Computer Vision and Image Processing
Introduction
Amazon Rekognition Basics
Rekognition and Deep Learning
Image Moderation
Facial Analysis
Celebrity Recognition
Face Comparison
Text in Images
Summary
Appendix
1. An Introduction to AWS
2. Analyzing Documents and Text with Natural Language Processing
3. Topic Modeling and Theme Extraction
5. Using Speech with the Chatbot
6. Computer Vision and Image Processing

1. An Introduction to AWS

Activity 1.01: Putting the Data into S3 with the CLI

Solution:

  1. Verify the configuration is correct by executing aws s3 ls to output your bucket name (the bucket name will be unique):

    Figure 1.46: Command line showing the list of files in an S3 bucket

    Note

    The list you will see maybe a little different from the preceding screenshot. It depends on what activities or exercises you have done; you might see a few more files in S3.

  2. Let's create a new S3 bucket. mb is the command for creating a bucket:
    aws s3 mb s3://lesson1-text-files-20200217

    Figure 1.47: Command to create an S3 bucket

    If it is successful, you will see the make_bucket : message.

    Note

    Your bucket name needs to be unique, so it is easier to append YYYYMMDD to the name (or something similar) to make it so. You will see this technique in later chapters as well. If you take a look at the earlier ls command, you will see the bucket names used — even used YYYYMMDDHHMM to ensure the...

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