Certification in Machine Learning and Data Science with AWS

Table of Contents

Learn Data Management on AWS, ML models on AWS, Advanced ML on AWS, Analytics and Visualization on AWS and Use Cases

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Take the next step in your cloud-powered AI and machine learning journey! Whether you're an aspiring data scientist, ML engineer, developer, or business leader, this course will equip you with the skills to harness AWS for scalable, real-world data science and machine learning solutions. Learn how services like SageMaker, Glue, Redshift, and QuickSight are transforming industries through data-driven intelligence, automation, and predictive analytics.

Guided by hands-on projects and real-world use cases, you will:

• Master foundational data science workflows and machine learning principles using AWS cloud services.

• Gain hands-on experience managing data with S3, Redshift, Glue, and building models with AWS SageMaker.

• Learn to train, optimize, and deploy ML models at scale using advanced tools like AutoML, hyperparameter tuning, and deep learning frameworks.

• Explore industry applications in e-commerce, finance, healthcare, and manufacturing using AWS AI/ML solutions.

• Understand best practices for cost management, security, and automation in cloud-based data science projects.

• Position yourself for a competitive advantage by building in-demand skills at the intersection of cloud computing, AI, and machine learning.

The Frameworks of the Course

· Engaging video lectures, case studies, projects, downloadable resources, and interactive exercises— designed to help you deeply understand how to leverage AWS for data science and machine learning applications.

· The course includes industry-specific case studies, cloud-native tools, reference guides, quizzes, self-paced assessments, and hands-on labs to strengthen your ability to build, manage, and deploy ML models using AWS services.

· In the first part of the course, you’ll learn the basics of data science, machine learning, and how AWS enables scalable cloud-based solutions.

· In the middle part of the course, you will gain hands-on experience using AWS tools like SageMaker, Glue, Redshift, and QuickSight to train, tune, and visualize ML workflows across different stages of a data science project.

· In the final part of the course, you will explore deployment strategies, automation pipelines, cost and security best practices, and real-world applications across industries. All your queries will be addressed within 48 hours with full support throughout your learning journey.

Course Content:

Part 1

Introduction and Study Plan

· Introduction and know your instructor

· Study Plan and Structure of the Course

Module 1. Introduction to Data Science and AWS

1.1. Basics of Data Science: Definitions, Workflows, and Tools

1.2. Overview of Machine Learning: Types, Algorithms, and Use Cases

1.3. Introduction to AWS Cloud and Its Benefits for ML and Data Science

1.4. Overview of Key AWS Services for Data Science and ML

1.5. Hands-On Activity: Set up an AWS account and explore the AWS Management Console.

1.6. Conclusion of Introduction to Data Science and AWS

Module 2. Data Management on AWS

2.1. Data Storage Solutions on AWS: S3, DynamoDB, and RDS

2.2. Data Warehousing with Amazon Redshift

2.3. Data Integration and ETL Processes with AWS Glue

2.4. Data Lake Architecture on AWS

2.5. Hands-On Activity: Create an S3 bucket and manage datasets.

Perform basic ETL using AWS Glue.

2.6. Conclusion of Data Management on AWS

Module 3. Introduction to AWS SageMaker

3.1. Overview of SageMaker Capabilities

3.2. Data Preparation and Labeling with SageMaker Data Wrangler

3.3. Building ML Models with SageMaker Studio

3.4. Pre-built Models and SageMaker JumpStart

3.5. Hands-On Activity: Load a dataset into SageMaker and explore it using Data Wrangler.

3.6. Conclusion of Introduction to AWS SageMaker.

Module 4. Building Machine Learning Models on AWS

4.1. Model Training and Tuning with SageMaker

4.2. Feature Engineering and Model Optimization

4.3. Hyper-parameter Tuning and AutoML with SageMaker

4.4. Managing Model Artifacts with SageMaker Model Registry

4.5 Hands-On Activity: Train a supervised learning model using SageMaker.

Perform hyperparameter tuning on the model.

4.6. Conclusion of Building Machine Learning Models on AWS

Module 5. Deploying and Scaling ML Models on AWS

5.1. Model Deployment with SageMaker Endpoints

5.2. Batch Transform for Large-Scale Inference

5.3. Real-Time Inference and Monitoring Deployed Models

5.4. Scaling Models with Elastic Inference and Multi-Model Endpoints

5.5. Hands-On Activity: Deploy an ML model on SageMaker and test it with sample inputs.

5.6. Conclusion of Deploying and Scaling ML Models on AWS

Module 6. Advanced Machine Learning on AWS

6.1. Deep Learning with AWS and SageMaker

6.2. Custom Training with TensorFlow and PyTorch in SageMaker

6.3. Reinforcement Learning with AWS DeepRacer

6.4. ML Pipelines for Automation and Workflow Management

6.5. Hands-On Activity: Build a simple deep learning model using SageMaker.

Explore reinforcement learning using AWS DeepRacer.

6.6. Conclusion of Advanced Machine Learning on AWS

Module 7. Analytics and Visualization on AWS

7.1. Data Analytics with AWS QuickSight

7.2. Log and Metric Analysis with CloudWatch and Athena

7.3. Integrating ML Models with Visualization Dashboards

7.4. Advanced Analytics Workflows with AWS Data Pipeline

7.5. Hands-On Activity: Create a visualization dashboard with AWS QuickSight.

7.6. Conclusion of Analytics and Visualization on AWS

Module 8. Security, Cost Management, and Best Practices

8.1. Ensuring Data Security with AWS IAM and Encryption

8.2. Managing Costs for Data Science and ML Projects on AWS

8.3. Best Practices for ML and Data Science Workflows on AWS

8.4. Common Pitfalls and How to Avoid Them

8.5.Hands-On Activity: Set up IAM roles and policies for secure ML workflows.

Monitor and optimize AWS costs using AWS Billing Dashboard.

8.6. Conclusion of Security, Cost Management, and Best Practices

Module 9. Real-World Use Cases and Applications

9.1. E-commerce: Customer Segmentation and Recommendation Systems

9.2. Finance: Fraud Detection and Risk Analysis

9.3. Healthcare: Predictive Analytics and Diagnostics

9.4. Manufacturing: Predictive Maintenance and Quality Control

9.5. Media and Entertainment

9.6. Education and Training

9.7. Hands-On Activity: Work on a domain-specific case study using AWS services

9.8. Conclusion of Real-World Use Cases and Applications.

Part 2

Capstone Project.