Why this course?

Description

cloud computing fundamentals with a focus on Amazon Web Services (AWS). Students will learn core services including EC2, Lambda, S3, Elastic Beanstalk, and SageMaker, building hands-on skills through practical labs and projects.

Starting from account setup, budgets, and IAM controls, students progress through compute services (EC2, Load Balancer, Auto Scaling), storage (S3), databases (RDS), and advanced cloud-native services like Lambda, EventBridge, and Glue ETL. The module also covers AWS security tools, machine learning with SageMaker, and developer tools. Capstone projects include CI/CD deployment, image resizing with Lambda, and chatbot creation with Lex.

AWS Learning Segments:

Introduction to Cloud Computing & AWS Account Creation

• Downloadable: Setup Guide and Budget Configuration

IAM & Budgeting in AWS

• Downloadable: IAM Configuration Materials

Virtual Servers with EC2

• PDF + Downloadable Guide

Load Balancers and Auto Scaling

• PDF + Downloadable Guide

Web App Deployment with Elastic Beanstalk

• PDF + Downloadable Guide

Storage Services in AWS (S3, etc.)

• PDF + Downloadable Guide

AWS Databases (RDS, DynamoDB)

• Downloadable: Database Setup Materials

AWS Lambda Introduction

• Downloadable Guide

Microservices, Containers, EventBridge

• PDF + Downloadable Resources

AWS Glue ETL Workflow

• PDF + Downloadable Guide

AWS Security Tools (Cognito, Secrets Manager, Inspector)

• Downloadable Guide

Machine Learning with SageMaker and Canvas

• Downloadable Guide

AWS AI Services Overview

• Downloadable Guide

AWS Developer Tools (CodeBuild, CodeDeploy, CodePipeline)

• Downloadable Guide

AWS Machine Learning Operations Segment

• Introduction to ML & SageMaker
• Python Libraries: Pandas, NumPy, Matplotlib, Seaborn
• Data Cleaning: Encoding, Scaling, Outliers, Null Values
• Model Building: Feature Selection, Train-Test Split, AWS Data Wrangler
• Algorithm Application: Traditional and SageMaker Built-in Models
• End-to-End MLOps with SageMaker Projects

AWS Projects:

• Census Income Prediction with CI/CD Deployment
• Music Rating Recommender System
• Image Resizer with Lambda Function
• SES and SNS Alert System with Lambda
• Amazon API Gateway Integration with Lambda
• Amazon QuickSight Dashboard Project
• Chatbot with AWS Lex
• Web App Deployment Using AWS Amplify

Section 3: Capstone Projects

Project 1: Train-Test Split Model

Objective: Use Python and ML libraries to split data for modeling.

• Downloadable + Assignment 20

Project 2: Credit Score Classification

Objective: Predict credit risk using classification models.

• Downloadable + Assignment 21

Project 3: Stress Prediction Using ML

Objective: Predict individual stress levels based on behavioral data.

• Downloadable + Assignment 21