Modeling with AWS Machine Learning

by Saravanan Dhandapani

Along with good working experience and knowledge of how to train and evaluate models, you need to have a good understanding of all the ML algorithms provided by AWS. This course will teach you the use cases of built-in algorithms provided by AWS.

What you'll learn

Being the front runner when it comes to cloud infrastructure, AWS has cutting edge services when it comes to machine learning. In this course, Modeling with AWS Machine Learning, you’ll learn to convert your data to an optimal model leveraging AWS SageMaker. First, you’ll explore supervised and unsupervised learning algorithms that are built-in to your AWS account and learn how to apply them to a specific business problem. Next, you’ll discover deep learning neural networks architecture and the built-in algorithms provided by AWS that cater specifically to computer vision and language processing domain. Finally, you’ll learn how to train a model on a SageMaker notebook, evaluate the model against the objective metric, and fine-tune the hyperparameters and arrive at an optimally performing model. When you’re finished with this course, you’ll have the skills and knowledge of all the AWS built-in algorithms and train, evaluate, and tune your models that are needed to master AWS SageMaker and clear AWS Machine Learning Specialty certification exam.

About the author

I have been working in IT design, development, and architecture for more than a decade for some of the top fortune 100 companies. I have been involved in fine-tuning software delivery process, improving performance and developing software that is scalable and portable. Some of the key areas where I have worked on are architecture and design using Java, ESB, Tomcat, ReactJS, JavaScript, Linux, Oracle, SVN, GIT and so on and cloud technologies including AWS and GCP.

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