Machine Learning and Data Science with AWS 2023 - Hands On

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Pranjal Srivastava,Harshit Srivastava

2:12:34

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1. Introduction

1. Introduction.mp4

01:27

2. Glue and Athena

1. Create a S3 bucket and add dataset.mp4

04:59

2. Create a Crawler using AWS Glue.mp4

05:14

3. Configuring output database name for crawler.mp4

03:23

4. Customize Schema, find table details in Glue and Log groups in Cloud Watch.mp4

06:37

5. Run SQL Queries on Athena and store output in S3 bucket.mp4

04:08

6. Create and Save Custom Query in AWS Athena.mp4

04:22

3. Data Preparations with Quicksight

1. Getting Started with Quicksight- installation.mp4

05:50

2. Importing dataset and understanding group and values.mp4

03:53

3. Creating Treemap and Customizing charts.mp4

04:59

4. Data Preparation- Editing Dataset before creating Charts.mp4

06:06

5. Create a Calculated Field using Functions- ceil and concat.mp4

05:44

4. Data Visualization with Quicksight

1. Map Chart and Conditional Formatting.mp4

06:37

2. Word Cloud.mp4

05:39

3. Funnel Chart.mp4

04:19

5. NLP Natural Language Processing

1. Build frontend for ML Application.mp4

05:27

2. Build Backend for ML Application.mp4

04:46

3. Add NLP task (translation).mp4

04:29

4. Demo Translation ML app.mp4

03:05

5. Creating Sentiment Analysis ML app.mp4

07:20

6. Demo Sentiment Analysis ML app.mp4

03:17

7. POS tagging ML App.mp4

08:06

6. AWS Deep Compose

1. Overview- deep composer.mp4

03:45

2. Getting started with Music Studio.mp4

04:53

3. Using GAN to generate other instruments.mp4

05:58

4. Generate creative melody and strech music using Transformers.mp4

04:28

5. Building a Training Model for MuseGAN and U-net.mp4

03:43

Description

Learn data science and machine learning services using AWS Athena, Glue, Quicksight, AWS Comprehend and Python Boto3

What You'll 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 is this for?

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

More details

Description
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 ITÂ 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. AWSÂ Translate is used for translating language from one language to another.
Who this course is for:
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

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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.

Harshit Srivastava

Instructor's Courses

I am Self-Taught developer who had worked on various platforms using varied languages, and involved in various Projects both Open Source and Proprietary.I have developed Web and Android Applications, chrome Extension, worked on various frameworks, fixed bugs for some projects, and explored numerous others. I think education and learning should be free and open, not be bound with restrictions like attending classes or going to college, People from all age groups, gender, faith, race, nations, etc must get equal privilege. When entire world would act this way like being a single FAMILY, we would truly realize VALUE of Knowledge and Human Life.Currently I am teaching more than 144,000 students from 189 countries across the world.

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