Learn NLP Natural Language Processing with AWS and Python

Learn NLP – Natural Language Processing with AWS and Python, available at $39.99, has an average rating of 4.05, with 17 lectures, based on 12 reviews, and has 4768 subscribers.

You will learn about Learn the fundamental concepts of natural language processing Learn how to use AWS NLP services like Comprehend, Transcribe, Translate, Polly and much more Learn how to do sentiment analysis of a phrase Learn to translate text into different languages Learn to create voice enable applications using Transcribe and Polly Learn how to create text to speech and speech to text functionality Learn to build NER – Named entity recognition Learn to create and use POS tagging Learn to use AWS SDK for Python with Boto3 library This course is ideal for individuals who are Machine Learning enthusiast or Cloud Architect or NLP engineer or ML engineer It is particularly useful for Machine Learning enthusiast or Cloud Architect or NLP engineer or ML engineer.

Enroll now: Learn NLP – Natural Language Processing with AWS and Python

Summary

Title: Learn NLP – Natural Language Processing with AWS and Python

Price: $39.99

Average Rating: 4.05

Number of Lectures: 17

Number of Published Lectures: 17

Number of Curriculum Items: 17

Number of Published Curriculum Objects: 17

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Learn the fundamental concepts of natural language processing

Learn how to use AWS NLP services like Comprehend, Transcribe, Translate, Polly and much more

Learn how to do sentiment analysis of a phrase

Learn to translate text into different languages

Learn to create voice enable applications using Transcribe and Polly

Learn how to create text to speech and speech to text functionality

Learn to build NER – Named entity recognition

Learn to create and use POS tagging

Learn to use AWS SDK for Python with Boto3 library

Who Should Attend

Machine Learning enthusiast

Cloud Architect

NLP engineer

ML engineer

Target Audiences

Machine Learning enthusiast

Cloud Architect

NLP engineer

ML engineer

Natural Language processing is one of the domain in Artificial Intelligence, that helps machine understand, interpret, manipulate and respond to human language.

We all are seeing more then thousands of Natural language applications such as signboard, menus, email, SMS, webpage, chats, reviews and so much more on daily life. NLP is everywhere. Natural language is not just about what we see, it also includes human speech as well. There are endless example of natural language application that can used in many use cases such as business and job opportunity.

NLP process the natural language by combining statistical, machine learning, and deep learning models and derive meaning from it.

In this course, you will learn about Natural language processing and it’s application with the help of AWS Comprehend, Translate, Transcribe and Polly.

What is AWS Comprehend ?

Amazon Comprehend is a natural-language processing (NLP) service that uses machine learning to identify the language of the text, extract key phrases, places, people, brands, or events, understand sentiment about products or services, and identify the main topics from a library of documents.

What is AWS Translate ?

Amazon Translate is a neural machine translation service that enables to translate between a source language and a target language .

What is AWS Transcribe ?

AWS transcribe service that helps to add speech to text functionality into the applications.

What is AWS Polly ?

Amazon Textract is a machine learning (ML) service that transform text into human voice.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Creating Frontend and backend for web application

Lecture 1: Build frontend for ML Application

Lecture 2: Build Backend for ML Application

Chapter 3: Translation ML app

Lecture 1: Add NLP task (translation)

Lecture 2: Demo: Translation ML app

Chapter 4: Sentiment Analysis ML app

Lecture 1: Creating Sentiment Analysis ML app

Lecture 2: Demo: Sentiment Analysis ML app

Chapter 5: POS tagging for NER (Named Entity Recognition) , word classification

Lecture 1: POS tagging ML App

Chapter 6: Named Entity Recognition

Lecture 1: Detect entity

Chapter 7: Transcribe (Speech to Text)

Lecture 1: Adding transcribe feature into web application

Lecture 2: Connecting to S3 bucket

Lecture 3: Uploading audio file to S3 bucket

Lecture 4: Demo: Transcribe ML app

Chapter 8: Polly

Lecture 1: Creating Polly function

Lecture 2: Adding Polly function into main application

Lecture 3: Demo: Polly ML app

Chapter 9: Resource File

Lecture 1: Resource File

Instructors

Pranjal Srivastava
Docker | Kubernetes | AWS | Azure | ML | Linux | Python

Rating Distribution

1 stars: 1 votes

2 stars: 0 votes

3 stars: 2 votes

4 stars: 8 votes

5 stars: 1 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

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