learning path

Introduction to Machine Learning on AWS

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Up to 6h 17m
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This course provides an introduction to Machine Learning concepts with a blend of instructional courses, quizzes, and hands-on labs.

We begin with an introduction to the concepts of machine learning. You will then learn how to implement The Amazon Machine Learning services to create and use machine learning models.

The course then provides a closer examination of Deep Learning and neural networks. The course includes two Labs where you will get hands-on experience working with neural networks. The “CPU vs GPU” lab highlights the performance benefit of training a neural network on a GPU. The “MXNet Style Images” Lab demonstrates an interesting use case in which a neural network can be utilized.

There is an assessment exam at the end of the course to help assess and validate your understanding of machine learning on AWS.

Intended Audience

This course is suited to anyone interested in getting started with machine learning concepts and services.

Learning Objectives

By completing this course you will be able to:

  • Recognize and explain the core concepts of machine learning.
  • Explain and apply the Amazon machine Learning service and Amazon distributed machine learning services.
  • Explain and apply supervised and unsupervised learning, classification and regression, algorithms, deep learning, and deep neural networks on AWS.

Prerequisites

Having an understanding of cloud concepts will help with your assimilation of this content. If you are new to cloud computing I suggest completing the What is Cloud Computing? Course first.

Content

This course includes 5 hours of High Definition video, 2 hands-on labs, quizzes, and an assessment exam. 

Feedback

We welcome all feedback so please direct any comments or questions on this course to us at support@cloudacademy.com

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About the Author

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Jeremy Cook, opens in a new tab
Content Lead Architect
Students
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Jeremy is a Content Lead Architect and DevOps SME here at Cloud Academy where he specializes in developing DevOps technical training documentation.

He has a strong background in software engineering, and has been coding with various languages, frameworks, and systems for the past 25+ years. In recent times, Jeremy has been focused on DevOps, Cloud (AWS, Azure, GCP), Security, Kubernetes, and Machine Learning.

Jeremy holds professional certifications for AWS, Azure, GCP, Terraform, Kubernetes (CKA, CKAD, CKS).

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