Machine Learning Engineer — AWS
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If you want to start building machine learning solutions on the AWS platform, this is the right place to start. It explores the concept of machine learning before moving on to the individual ML services offered by AWS.

You learn about the main principles of machine learning including supervised and unsupervised learning, ML algorithms, deep learning, and neural networks. We'll then focus on AWS itself and learn how to use it to feed data into an ML model and make accurate predictions based on the data.

We then delve deeper into the topic by exploring the AI services available in AWS such as Rekognition, Lex, and Elastic Map Reduce. You'll learn how to build an ML model, automate image labeling, and build a chatbot.

We'll also take a comprehensive look at Amazon SageMaker and you'll have plenty of opportunities to use the service yourself thanks to our hands-on labs.

This selection of content is ideal for anyone looking to become a machine learning engineer or who simply wants to learn more about the machine learning capabilities of AWS. If that sounds like you, then hit the Start Training Plan button below!

Average completion time (studying 3 hours a week)
34 working days
Content Duration
19h 57m
Machine Learning Engineer — AWS
Content:
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2
Exams
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3
Learning Paths
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Pre-Test: Machine Learning Engineer - AWS
Pre-Test: Machine Learning Engineer - AWS
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Introduction to Machine Learning on AWS
Learn the tools and practices of Machine Learning on Amazon Web Services with a blend of instructional courses, quizzes, and hands-on labs.
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Applying Machine Learning and AI Services on AWS
Learn practical applications of AWS machine learning and Artificial Intelligence services using a blend of learning material and hands-on labs.
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Start Modelling Data with Amazon SageMaker
Start manipulating and modelling data with Amazon SageMaker
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Post-Test: Machine Learning Engineer - AWS
Post-Test: Machine Learning Engineer - AWS