Introduction to Machine Learning on AWS

OverviewStepsAuthor
DifficultyBeginner
AVG Duration6h
Students1815
Ratings
5/5
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Content
612

Description

Overview
This learning path 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 learning path then provides a closer examination of Deep Learning and neural networks. The learning path 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 learning path to help assess and validate your understanding of machine learning on AWS.

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

Learning Objectives
By completing this learning path 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.

Pre-requisites
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 learning path 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 

Certificate

Your certificate for this learning path
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Training Content

1
Course - Beginner - 2m
Introduction to Machine Learning on AWS Learning Path
This course provides an overview of the Introduction to Machine Learning on AWS Learning Path.
2
Course - Beginner - 48m
Introduction to Machine Learning Concepts
In this course, you'll learn about Machine Learning and where it fits within the wider Artificial Intelligence (AI) field.
3
Course - Beginner - 37m
Introduction to Data and Machine Learning
This course has been expertly created to provide you with a strong foundation in machine learning and deep learning.
4
Course - Beginner - 1h 23m
Module 0 - What is Machine Learning? - Part One
This course is the first in a two-part series covering the fundamentals of machine learning.
5
Course - Beginner - 1h 30m
Module 0 - What is Machine Learning? - Part Two
This course is part two of the module on machine learning and covers unsupervised learning, the theoretical basis for machine learning, model and linear regression, the semantic gap, and how we approximate the truth.
6
Hands-on Lab - Beginner - 45m
Analyzing CPU vs GPU Performance for AWS Machine Learning
Take control of a p2.xlarge instance equipped with an NVIDIA Tesla K80 GPU to perform CPU vs GPU performance analysis for AWS Machine Learning in this Lab.
7
Hands-on Lab - Intermediate - 1h 10m
Using an MXNet Neural Network to Style Images
Join this Lab and gain experience using an MXNet convolutional neural network to style images and monitor the GPU used for training in Amazon CloudWatch.
8
Course - Beginner - 2m
Review for Introduction to Machine Learning on AWS Learning Path
This course provides a quick review and summary of what was learned during the Introduction to Machine Learning on AWS Learning Path.
9
Exam - 30m
Final Exam: Introduction to Machine Learning on AWS
Final Exam: Introduction to Machine Learning on AWS
About the Author
Students70507
Labs44
Courses105
Learning paths52

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, GCP, Azure), Security, Kubernetes, and Machine Learning.

Jeremy holds professional certifications for AWS, GCP, and Kubernetes.