AWS for Machine Learning

Specifically designed to help you prepare for the AWS Machine Learning Specialty Certification, this learning path provides interactive content comprised of hands-on labs and lectures.

AWS Machine Learning syllabus

AWS for Machine Learning course explores how to use the machine learning to solve a real business problem in a project-based learning environment.

📊 This program validates your ML skills in the cloud. In this course, you’ll learn:

Hands-on tasks designed to challenge ML intuition, creativity and knowledge of the AWS platform.

Design and implement scalable, cost-optimized, reliable, and secure ML solutions.

The domains of knowledge for the AWS Certified Machine Learning Speciality exam.

Best practices for using the tools and platforms of AWS for data engineering, data analysis, machine learning modeling, model evaluation and deployment.

📍With this course you’ll get a solid understanding of the services and platforms available on AWS for Machine Learning projects, build a foundation to pass the certification exam and feel equipped to use the AWS ML portfolio in your own real-world applications.

Students will learn about each phase of the process pipeline from instructor demonstrations and then apply that knowledge to complete a project solving various business problems.

Projects that you will add to your portfolio:

Analyzing Azerbaijani text on AWS with Amazon Comprehend

Turning Speech into Text on AWS with Amazon Transcribe

Extracting Text and Data with Amazon Textract

Customer Churn Prediction with SageMaker Studio XGBoost Algorithm

Building a Content Recommendation System with NTM (Neural Topic Model)

Understanding Trends in Company Valuation with NLP

Building a traffic sign classifier model using Sagemaker

Building real-time Data Processing Application with Kinesis Stream and AWS Lambda

Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)

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