Amazon Sagemaker: Create and Deploy Machine Learning Today

What you'll learn
Know 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!
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

Description
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!

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

Amazon Sagemaker: Create and Deploy Machine Learning Today Fast Download
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