Use artificial intelligence and machine learning on AWS to create engaging applications
- 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
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.
Table of Contents
Introduction to Amazon Web Services
- What is AWS?
- What is Amazon S3?
- Core S3 Concepts
- Data Replication
- AWS Command-Line Interface (CLI)
- Command Line-Interface (CLI) Usage
- Recursion and Parameters
- Using the AWS Console to Identify Machine Learning Services
Summarizing Text Documents Using NLP
- What is Natural Language Processing?
- Using Amazon Comprehend to Inspect Text and Determine the Primary Language
Extracting Information in a Set of Documents
- Detecting Named Entities – AWS SDK for Python (boto3)
- DetectEntites – Input and Output
- Exercise 7: Determining the Named Entities in a Document
- DetectEntities in a Set of Documents (Text Files)
- Detecting Key Phrases
- Exercise 8: Determining the Key Phrase Detection.
- Detecting Sentiments
- Exercise 9: Detecting Sentiment Analysis
Setting up a Lambda function and Analyzing Imported Text Using Comprehend
- What is AWS Lambda?
- What does AWS Lambda do?
- Lambda Function Anatomy
- Exercise 10: Setting up a Lambda function for S3
- Exercise 11: Configuring the Trigger for an S3 Bucket
- Exercise 12: Assigning Policies to S3_trigger to Access Comprehend
- Activity 3: Integrating Lambda with Amazon Comprehend to Perform Text Analysis
Perform Topic Modeling and Theme Extraction
Extracting and Analyzing Common Themes
- Topic Modeling with Latent Dirichlet Allocation (LDA)
- Basic LDA example
- Why Use LDA?
- Amazon Comprehend–Topic Modeling Guidelines
- Exercise 13: Topic Modeling of a Known Topic Structure
- Exercise 14: Performing Known Structure Analysis
- Activity 4: Perform Topic Modeling on a Set of Documents with Unknown Topics
Creating a Chatbot with Natural Language
- What is a Chatbot?
- What is Natural Language Understanding?
- Setting Up with Amazon Lex
- Creating a Custom Chatbot
- A Bot Recognizing an Intent and Filling a Slot
Lambda Function – Implementation of Business Logic
- Exercise 17: Creating a Lambda Function to Handle Chatbot Fulfillment
- Implementing the Lambda Function
- Input Parameter Structure
- Implementing the High-Level Handler Function
- Implementing the Function to Retrieve the Market Quote
- Returning the Information to the Calling App (The Chatbot)
- Connecting to the Chatbot
- Activity 5: Creating a Custom Bot and Configuring the Bot
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
Analyzing Images with Computer Vision
- Amazon Rekognition Basics
Rekognition and Deep Learning
- Detect Objects and Scenes in Images
- Exercise 21: Detecting Objects and Scenes using your own images
- Image Moderation
- Exercise 22: Detecting objectionable content in images
- Facial Analysis
- Exercise 23: Analyzing Faces in your Own Images
- Celebrity Recognition
- Exercise 24: Recognizing Celebrities in your Own Images
- Face Comparison
- Activity 1: Creating and Analyzing Different Faces in Rekognition
- Text in Images
- Exercise 25: Extracting Text from your Own Images
- Chapter 1: Introduction to Amazon Web Services
- Chapter 2: Summarizing Text Documents Using NLP
- Chapter 3: Perform Topic Modeling and Theme Extraction
- Chapter 4: Creating a Chatbot with Natural Language
- Chapter 5: Using Speech with the Chatbot
- Chapter 6: Analyzing Images with Computer Vision
- Title: Machine Learning with AWS
- Release date: November 2018
- Publisher(s): Packt Publishing
- ISBN: 9781789806199