This repository contains the implementation of various AWS AI Services.

This chapter covers the Amazon Rekognition service for analyzing the content of the images using various techniques. You will learn how to analyze faces and recognize celebrities in images. You will also be able to compare faces in different images to see how closely they match with each other.

Live Streamed Alexa Skill development to create a searchable index of the Alexa Office Hours Archives

Analyse user sentiments and identify entities on subreddits using AWS serverless architecture.

This module looks at how use Amazon Connect, Lex, and Lambda to interact with a chatbot using voice. You will create a personal call center using Amazon Connect and you will learn how to connect the call center to your Lex chatbot

Sentiment analysis using BOTO3 for Python on Amazon Comprehend

Scaling sentiment analysis with AWS Glue and Amazon Comprehend.

This module teaches you how to design a chatbot using Amazon Lex by following the best design practices for conversational AI. You will start by learning the basics of chatbots. Then, you will use Amazon Lex to create a custom chatbot that gets the latest stock market quotes by recognizing the intent in text

In this module you will look at AWS AI services and examine an emerging computing paradigm – the Serverless Computing. We will then proceed to applying NLP and the Amazon Comprehend service to analyze documents.

A benchmark comparison project among the most popular sentiment analysis engines: VaderSentiment, TextBlob, Azure Text Analysis and Amazon Comprehend. The benchmarker is a python module that supports 3 datasets: IMDb, Sentiment140 and Twitter.

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Our first Challenges of building AI-Powered APP

Program to analyze tweet sentiment

Example php scripts making sigv4 signed aws api requests

Dataiku DSS plugin to use the Amazon Comprehend APIs 📚

Front-end website | Backend API | Authentication | Backend compute functions | Asynchronous reporting workflow | Distributed tracing | Monitoring features | Improving performance

In this module you will learn how to analyze topic modeling output from Amazon Comprehend, then perform topic modeling on two documents with a known topic structure.

Provides a reliable political feed for readers to become knowledgeable and informed voters on the state political level.

Final Project - Evolution of Banking vs Evolution of Blockchain Based Finance

This construct creates the foundation for developers to explore the combination of Amazon S3 Object Lambda and Amazon Comprehend for PII scenarios and it is designed with flexibility, i.e, the developers could tweak arguments via CDK to see how AWS services work and behave.