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Description

We all know that Machine Learning (ML) and Artificial Intelligence (AI) are the two fastest-growing areas in technology & the most desirable skillset in the present digital job market. You will find a number of ML and AI resources online nowadays, with a wide variety of not only online courses, but also certificates.


What is Amazon Sage Maker?

Amazon Sage Maker is a fully managed service launched by Amazon based on its two decades of experience developing real-world machine learning applications, including personalization, product recommendations, intelligent shopping, robotics, & voice-assisted devices. In simple terms, Sage Maker is an online platform that enables developers & data scientists to quickly & easily build, tune, train, and deploy (ML) machine learning models.

As part of the AWS (Amazon Web Services) certification, you can get started with Amazon Sage Maker certification. This certification is highly recommended for fresher as well as professionals in the industry.

Is Sage Maker Certification Worth It?

Definitely yes! Learning the foundations of Machine Learning with this certification will help professionals, as well as freshers, keep pace with the on-going industry growth, expand their skills & even help advance their careers.

Skills You Will Learn

This course teaches the certification seekers about ways to get started with AWS ML Techniques.

Key topics covered:
  • Computer Vision on AWS,
  • Machine Learning on AWS,
  • Natural Language Processing or (NLP) on AWS.
The best part is you will get a chance to practice implementing them & getting them to work for yourself and prepare better for examinations.

All said & done, seeking Amazon Sage Maker certification is a smart move for all experts associated with the IT industry.

Course Content

Total: 26 lectures
  • Introduction
  • Getting Started on Sagemaker's Studio
  • What Sagemaker looks like and tools available
  • Jupiter Notebooks on Sagemaker
  • Picking an algorithm on Sagemaker
  • Setting Hyperparameters
  • Teaching the computer with Training Data and Bias
  • 3 Types of testing on the model you can do
  • Automatic Model Tuning and Defining Metrics
  • Defining Hperparameter Ranges and Scaling Types
  • Creating an S3 bucket
  • What is Sagemaker's Jumpstart?
  • Performing Common Tasks on Sagemaker's Studio
  • Amazon's Behind the scenes code
  • Creating a Hyperparameter Tuning Job, Bank Scenario
  • Stopping Training Jobs Early
  • Best Secrets for Hyperparameter Tuning
  • Warm Start Hyperparameter Tuning Jobs
  • Creating a warm start tuning job: low level Amazon Sagemaker API for Python Boto 3
  • Creating a Warm start Tuning job: using Amazon Sagemaker Python SDK
  • Deploying a Model
  • Autopilot
  • Creating a Labeling Job
  • Introduction to Data Wrangler
  • Data Wrangler Demo
  • Amazon Augmented AL

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