Cloud Academy – Introduction to Machine Learning on AWS
English | Size: 1.89 GB
Category: Tutorial
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.
Miscellaneous
Provider: Cloud Academy
Title: Introduction to Machine Learning on AWS
Knowledge must be free. We are a 0day e-learning release group.
You can find us on Discord and pass some exams together. You will know when you found us.
Internal ID: CA126
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