Amazon Sagemaker: Create and Deploy Machine Learning Today

Are you looking to get into AWS Sagemaker, with no experience, and want to see if you like what Sagemaker is all about? Or do you know that Sagemaker is where you’re future is headed but want to learn foundation skills needed for a career in machine learning?

But you have so many options out there for learning Sagemaker.

Why this course?

Because this course will be fun and interactive, lively, and teach in a way to make some of the most complex tools and features of Sagemaker easy to use, because to take a step forward in you’re career you should fall in love with what you do, and that’s what I’m hoping to create with this course.

What will this course cover?

You will learn:

• How to pick which of Sagemaker’s algorithm to use
• Be able to create a Juypter notebook.
• Be able to create an encryption key.
• Utilize deep learning frameworks within Sagemaker.
• Fix training data bias using Sagemaker’s features.
• Understand the purpose of Sagemaker’s Clarify?
• Choose whether to do Online testing with live data or offline testing or do Machine Learning on a holdout set.
• How to define a Hyperparameter range
• Understand the different types of ScalingTypes you can use
• Learn how to create an S3 bucket using 2 methods!
• Be able to create a hyperparameter tuning job
• Use best training jobs to create a model
• Be able to stop a training job early and save time
• Understand best practices for hyperparameter tuning jobs: what kind of range to use!
• You Understand the different WarmStart Hyperparameter tuning Jobs and what they do.
• Understand IDENTICAL_DATA_AND_ALGORTITHM and TRANSFER_LEARNING
• Use Sagemaker’s Autopilot feature
• Be able to deploy a model
• Use JumpStart
• Be able to use Data Wrangler
• Import, Prepare, Analyze, and Transform data with Data Wrangler
• Understand Augmented AI