AWS machine learning with data analytics Course

Learn how to blend machine learning and data analytics on the AWS cloud, through this comprehensive course that takes participants through the skills needed to develop intelligent data-first solutions. This course is offered by the Best Corporate Training Company and acknowledged as the Best Training Company.

In this course the participants will understand and experience all applicable aspects of machine learning workflow using effective AWS services. Participants will have hands-on experience using AWS SageMaker for MLOps, to automate, monitor, and scale machine learning pipelines.

Participants will learn the effective ways to build and govern centralized data repositories using AWS Lake Formation for ingestion and data catalogs. Participants will have extensive experience using AWS Glue and DataBrew for data prep, cleaning, and transforming data so it can be used for analytics or to train ML models.

Participants will learn how to use Amazon Redshift to create high-performance scalable data warehouses and integrate ML capabilities into analytical workflows with a hands-on perspective.

This course is ideal for professionals working with excessive volumes of data wanting to derive intelligent insights, and additional strategies to understand what it means to deploy, and manage effective ML and analytics solutions in near real-time environments that can create measurable business outcomes, through the AWS platform. 


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Learning Options for You

  • Live Training (Duration : 44 Hours)
  • Per Participant

Fee: On Request

Course Prerequisites

  • Basic knowledge of Python programming
  • Understanding of statistics and data analysis fundamentals
  • Familiarity with cloud computing concepts (preferably AWS)
  • Basic knowledge of databases and data visualization tools

Learning Objectives

AWS Machine Learning with Data Analytics is a comprehensive course designed to help learners build intelligent, scalable ML solutions using AWS cloud services. You will learn data collection, preprocessing, model training, evaluation, and deployment using tools like AWS SageMaker, Glue, Redshift, and Athena. This course bridges the gap between analytics and machine learning, enabling professionals to deliver actionable business insights and automated workflows across industries.

Target Audience

  • Data analysts aiming to integrate machine learning into analytics workflows
  • AI/ML beginners looking to build skills using AWS services
  • Cloud professionals who want to specialize in AWS Machine Learning
  • Business analysts and BI professionals seeking data-driven automation
  • Developers and engineers working on predictive analytics projects
  • Students and professionals preparing for AWS ML & Data Analytics certifications

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