Learn Computer Vision and NLP with AWS ML and Python Boto3

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Pranjal Srivastava

1:58:37

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1. Introduction

1. Introduction.mp4

01:49

2. Creating Frontend and backend for web application

1. Build frontend for ML Application.mp4

05:27

2. Build Backend for ML Application.mp4

04:46

3. Translation ML app

1. Add NLP task (translation).mp4

04:29

2. Demo Translation ML app.mp4

03:05

4. Sentiment Analysis ML app

1. Creating Sentiment Analysis ML app.mp4

07:20

2. Demo Sentiment Analysis ML app.mp4

03:17

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

1. POS tagging ML App.mp4

08:06

6. Named Entity Recognition

1. Detect entity.mp4

05:15

7. Transcribe (Speech to Text)

1. Uploading audio file to S3 bucket.mp4

10:14

2. Creating Transcribe job.mp4

06:30

3. Understanding ASR output.mp4

01:49

4. Adding transcribe feature into web application.mp4

05:39

5. Demo Transcribe ML app.mp4

01:50

8. Polly

1. Creating Polly function.mp4

05:50

2. Adding Polly function into main application.mp4

04:42

3. Demo Polly ML App.mp4

03:58

9. AWS Rekognition Keyword Generator App

1. Creating the frontend of Keyword Generator App.mp4

08:18

2. Generating keywords using python script.mp4

05:12

3. Creating Upload function.mp4

13:08

4. Demo Keyword Generator application.mp4

07:53

Description

Learn AWS Machine learning services like AWS Comprehend, Translate, Transcribe, Rekognition and Polly with Python Boto3

What You'll 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 is this for?

Machine Learning enthusiast

Cloud Architect

NLP engineer

ML engineer

What You Need to Know?

Must have python and required python lib installed on system

AWS CLI installed on system

Knowledge of NLP and computer vision would be helpful. But not necessary.

More details

Description
Natural Language processing and Computer Vision are indispensable domain in Artificial Intelligence, NLP that helps machine understand, interpret, manipulate and respond to human language whereas Computer Vision helps in extraction of information and insights from an images and videos.
In this course, you will learn about Natural language processing & Computer Vision and it's application with the help of AWS Comprehend, Translate, Transcribe, Rekognition 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.

What is AWS Rekognition?
Amazon Rekognition is a service that makes it easy to add powerful visual analysis to your applications. Rekognition Image lets you easily build powerful applications to search, verify, and organize millions of images. Rekognition Video lets you extract motion-based context from stored or live stream videos and helps you analyze them.

Who this course is for:
Machine Learning enthusiast
Cloud Architect
NLP engineer
ML engineer

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I am an Instructor, Devops engineer, machine learning enthusiast, cloud expert and passionate developer.I have authored 73 courses with over 102,000 students worldwide across 175+ countries on wide array of technologies like containerization, machine learning, Linux, programming languages and cloud computing platforms like Microsoft Azure, Amazon Web Service and IBM Cloud.

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