Certification in Machine Learning and Data Science with AWS
Learn Data Management on AWS, ML models on AWS, Advanced ML on AWS, Analytics and Visualization on AWS and Use Cases
11
students
7.5 hours
content
Jul 2025
last update
$219.99
regular price
What you will learn
You will learn about the Introduction to Data Science and AWS, including the fundamentals of data science workflows, tools, and techniques
You will also explore the benefits of using AWS Cloud for ML and data science and become familiar with key AWS services that support these functions.
As part of the hands-on activity, you will set up your AWS account and explore the AWS Management Console.
You will explore Data Management on AWS, including how to store, organize, and manage data using services like Amazon S3, DynamoDB, and RDS
You will understand how data warehousing works with Amazon Redshift and how to implement ETL pipelines using AWS Glue.
You will also study the architecture and use cases of data lakes on AWS. Hands-on tasks include creating and managing an S3 bucket and performing ETL operations
You will get introduced to AWS SageMaker, Amazon's integrated ML platform, and explore its full capabilities.
Learn preparing and labeling data with SageMaker Data Wrangler, using SageMaker Studio to build and analyze ML models, and leveraging pre-trained models
You will learn how to Build Machine Learning Models on AWS using SageMaker, focusing on model training, tuning, and optimization
You will also learn to manage model versions using SageMaker Model Registry. In the hands-on exercise, you will train a supervised model
You will understand how to Deploy and Scale ML Models on AWS, including strategies for deploying models using SageMaker Endpoints
Learn handling large-scale inference with Batch Transform, and enabling real-time predictions. You will explore scaling techniques such as Elastic Inference
You will explore Advanced Machine Learning on AWS, including building deep learning models using TensorFlow and PyTorch in SageMaker
You will also study how to automate workflows using ML pipelines. As part of your hands-on work, you will build a simple deep learning model
You will learn how to perform Analytics and Visualization on AWS using services like QuickSight for dashboards, CloudWatch for monitoring
You will also learn to integrate ML models with visualization tools and set up advanced analytics workflows using AWS Data Pipeline
You will study Security, Cost Management, and Best Practices, learning how to secure data science workflows using IAM roles, encryption
You will also review best practices and common pitfalls in AWS-based ML workflows. Hands-on tasks include setting up IAM policies and monitoring project cost
You will analyze Real-World Use Cases and Applications of AWS Data Science across domains such as e-commerce, finance, healthcare, manufacturing etc
By the end of this course, you will apply all your skills to a capstone project, where you will solve a real-world problem or build a complete machine learning
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6715317
udemy ID
11/07/2025
course created date
15/07/2025
course indexed date
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