Beginning Machine Learning with AWS

Let us begin the second lesson and take a brief look at the topic coverage.     

Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. Let us learn about the concept of NLP in detail and understand its implementation in Amazon Comprehend.   

Now that you have the basic understanding of how Comprehend works, let us inspect and determine the primary language and the supported languages. In this topic, we will detect the dominant language using the CLI interface in a single and multiple text documents.   

Now, let us learn about detecting named entities, how to input them and process them to get the desired output. We will do this with an exercise of detecting named entities in single and multiple documents.

Detecting key phrases and sentiments is an important element of using NLP. Let us dive deep and learn to detect key phrases and perform sentiment analysis.

Now, let us learn about AWS Lambda. In this topic, we will cover the concept, its usage and the anatomy of a Lambda function.   

Let us look at the architectural diagram of a Lambda function. We will set-up a Lambda function for S3, configure the trigger for an S3 bucket and assign policies to S3 trigger to access comprehend.   

This video summarizes your learning of this lesson.   

Let us begin the third lesson and take a brief look at the topic coverage.   

Let us now learn extracting and analyzing common themes and also will understand about Latent Dirichlet allocation (LDA).

Now that we know about Latent Dirichlet Allocation let us learn about topic modeling with LDA with a basic example and why we should use LDA.   

There are some guidelines to be understood for topic modeling and it has input and output format options. We will be able to see a sample of the modelling output. Then we will demonstrate a topic modeling of a known topic structure and perform known structure analysis.

This video summarizes your learning of this lesson.     

Let us begin with our third lesson and take a brief look at the topic coverage.     

Here, you will learn about a chatbot and also you will learn about its core concepts. Later we will know about Natural Language Understanding (NLU).

Now that you know what is a chatbot, let us set up with amazon Lex and create a sample chatbot. We also will create a custom chatbot and its workflow. Lastly we will create a bot that will recognize an intent and fill a slot.   

Let us now learn about implementation of business logic in lambda function and create a lambda function to handle chatbot fulfillment.

This video summarizes your learning of this lesson.   

Let us begin the fifth lesson and take a brief look at the topic coverage.   

Let us now learn about amazon connect and its key features. We also will look at the free tier services which amazon gives us. Later we will create an interaction with the chatbot and how to talk to your chatbot through a call center using Amazon Connect. Lastly we will go ahead and create a personal call center and obtain a free phone number for your call center.   

Now, let us learn about Amazon Lex and how to use its chatbots with Amazon Connect. Then we will understand what are contact flows and nodes.   

Now that you know about contact flow let us learn about the various templates of contact flow that are available. It also demonstrates how to connect the call center to your Lex chatbot.

This video summarizes your learning of this lesson.

Let us begin the sixth lesson and take a brief look at the topic coverage.   

Let us understand what is Amazon Rekognition Basics and its use cases. Also we will learn about the free tier information on Amazon Rekognition.

Now that you know about Amazon Rekognition, let us understand its features. Amazon Rekognition provides a feature that can detect objects and scenes in images. We will also learn briefly about Deep Learning. Lastly we will learn about the types of labels supported, image moderation and hierarchical taxonomy.   

Let us learn how to use facial analysis and celebrity recognition features. Rekognition can perform more detailed analysis on faces as well and provides the ability to recognize and label celebrities and other famous people in images.

Rekognition allows you to compare faces in two images. This is mainly for the purpose of identifying which faces are the same in both images. Learn about the restrictions on usage of this feature and how to detect and recognize text in images. Text in image is a feature of Amazon Rekognition that recognize text such as street names, captions, product names, and vehicular license plates in an image.   

This video summarizes your learning of this lesson.