Machine Learning and Data Science with AWS- Hands On

Machine Learning and Data Science with AWS- Hands On, available at $39.99, has an average rating of 3.8, with 36 lectures, based on 76 reviews, and has 6257 subscribers.

You will learn about You could prepare your dataset using AWS Glue, and Quicksight Perform Data Analysis using Athena Could Create Data Visualization Charts with Quicksight You could create and develop machine learning models using Natural Language Processing This course is ideal for individuals who are Anyone who is curious to learn Data Science and Machine Learning on AWS or Student and IT professionals curious to learn AWS cloud services for Machine learning and Data Science or People interested in learning AWS Glue, Athena, Quicksight, Amazon NLP and Computer Vision It is particularly useful for Anyone who is curious to learn Data Science and Machine Learning on AWS or Student and IT professionals curious to learn AWS cloud services for Machine learning and Data Science or People interested in learning AWS Glue, Athena, Quicksight, Amazon NLP and Computer Vision.

Enroll now: Machine Learning and Data Science with AWS- Hands On

Summary

Title: Machine Learning and Data Science with AWS- Hands On

Price: $39.99

Average Rating: 3.8

Number of Lectures: 36

Number of Published Lectures: 36

Number of Curriculum Items: 37

Number of Published Curriculum Objects: 37

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

You could prepare your dataset using AWS Glue, and Quicksight

Perform Data Analysis using Athena

Could Create Data Visualization Charts with Quicksight

You could create and develop machine learning models using Natural Language Processing

Who Should Attend

Anyone who is curious to learn Data Science and Machine Learning on AWS

Student and IT professionals curious to learn AWS cloud services for Machine learning and Data Science

People interested in learning AWS Glue, Athena, Quicksight, Amazon NLP and Computer Vision

Target Audiences

Anyone who is curious to learn Data Science and Machine Learning on AWS

Student and IT professionals curious to learn AWS cloud services for Machine learning and Data Science

People interested in learning AWS Glue, Athena, Quicksight, Amazon NLP and Computer Vision

Welcome to this course on Machine Learning and Data Science with AWS. Amazon Web services or AWS is one of the biggest cloud computing platform where everything gets deployed to scale and action. Understanding the concepts and methods are vital, but being able to develop and deploy those concepts in forms of real life applications is something that is most weighted by the industry. Thus, here in this course, we are focused on ways you can use various cloud services on AWS to actually build and deploy you ideas into actions on multiple domains on Machine Learning and Data Science. You could be an IT professional looking for job change or upgrading your skillset or you could be a passionate learner or cloud certification aspirant, this course is for wider audience that if formed by the people who would like to learn any of these or a combination of these things-

Create and Analyze dataset to find insights and spot outliers or trends

Build Data visualization reports and dashboards by combining various visualization charts to represent data insights

Develop machine learning models for Natural Language Processing for various applications on AWS

And much more.

Course Structure

This course consists of multiple topics that are arranged in multiple sections. In the first few sections you would learn cloud services related to Data Science and Analysis on AWS with hands on practical examples. There you would be learning about creating a crawler in Glue, Analyzing dataset using SQL in Amazon Athena. After that you would learn to prepare a dataset for creating Data Visualization charts and reports that can be used for finding critical insights from the dataset that can be used in decision making process. You will learn to create calculated fields, excluded lists and filters on AWS Quicksight, followed by some advanced charts such as Word cloud and Funnel chart.

After that in Machine Learning section, you will learn about Natural language processing and it’s application with the help of AWS Comprehend and Translate. AWS Comprehend is used 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. AWS Translate is used for translating language from one language to another.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Glue and Athena

Lecture 1: Create a S3 bucket and add dataset

Lecture 2: Create a Crawler using AWS Glue

Lecture 3: Configuring output database name for crawler

Lecture 4: Customize Schema, find table details in Glue and Log groups in Cloud Watch

Lecture 5: Run SQL Queries on Athena and store output in S3 bucket

Lecture 6: Create and Save Custom Query in AWS Athena

Chapter 3: Data Preparations with Quicksight

Lecture 1: Getting Started with Quicksight- installation

Lecture 2: Importing dataset and understanding group and values

Lecture 3: Creating Treemap and Customizing charts

Lecture 4: Data Preparation- Editing Dataset before creating Charts

Lecture 5: Create a Calculated Field using Functions- ceil and concat

Chapter 4: Data Visualization with Quicksight

Lecture 1: More calculated fields

Lecture 2: Creating Filters and Excluded list

Lecture 3: Map Chart and Conditional Formatting

Lecture 4: Pivot table

Lecture 5: using Conditional formatting

Lecture 6: Word Cloud

Lecture 7: Funnel Chart

Chapter 5: NLP Natural Language Processing

Lecture 1: Build frontend for ML Application

Lecture 2: Build Backend for ML Application

Lecture 3: Add NLP task (translation)

Lecture 4: Demo: Translation ML app

Lecture 5: Creating Sentiment Analysis ML app

Lecture 6: Demo: Sentiment Analysis ML app

Lecture 7: POS tagging ML App

Lecture 8: Detect entity

Chapter 6: AWS Deep Compose

Lecture 1: Overview- deep composer

Lecture 2: Getting started with Music Studio

Lecture 3: Using GAN to generate other instruments

Lecture 4: Generate creative melody and strech music using Transformers

Lecture 5: Building a Training Model for MuseGAN and U-net

Chapter 7: Computer Vision with AWS

Lecture 1: Creating the frontend of Keyword Generator App

Lecture 2: Generating keywords using python script

Lecture 3: Creating Upload function

Lecture 4: Demo: Keyword Generator application

Instructors

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

Harshit Srivastava
Cloud Professional | Content Creator | Edupreneur

Rating Distribution

1 stars: 2 votes

2 stars: 3 votes

3 stars: 6 votes

4 stars: 27 votes

5 stars: 38 votes

Frequently Asked Questions

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