CloudAcademy

Amazon Machine Learning for Human Activity Recognition

The hands-on lab is part of this learning path

Big Data – Specialty Certification Preparation for AWS

course-steps 14 lab-steps 5 quiz-steps 3

Lab Steps

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Logging in to the Amazon Web Services Console
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Machine Learning Concepts
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Dataset selection and manipulation
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Creating a Datasource, Model, and Evaluation
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Training and Evaluating your first Model
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Generating online Predictions
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Cleaning up the ML Environment

Ready for the real environment experience?

DifficultyIntermediate
Duration2h
Students577

Description

Machine Learning is a very powerful technology to drive your data-driven decisions

Lab Overview

Nowadays it's possible for anyone to exploit the huge volumes of information available through big data and open datasets, whether you are a data scientist, an enterprise developer, or a small startup. Amazon Machine Learning lets you focus completely on your data, without wasting your time with countless trial models or complicated math. Amazon Machine Learning as a service offers great potential in making applications smarter, by making it easy to use for developers of all skill levels.

This Lab will offer a very brief overview of the main machine learning concepts, and then use an open dataset from UCI to train and use a real-world model for HAR (Human Activity Recognition). You will walk through the whole process, from the dataset analysis and Datasource creation, all the way to model training/evaluation, and execute a Python script to generate real time predictions. This Lab will give you a general understanding of machine learning that should lead to ideas of how to use Amazon Machine Learning to build out models for your own environment.

Lab Objectives

Upon completion of this Lab you will be able to:

  • Understand basic Machine Learning (ML) concepts and ML phases (process flow)
  • Know the importance of data manipulation prior to using Amazon ML
  • Create a Datasource for ML input
  • Use the Amazon ML Management Console to visualize your data
  • Explore the performance of your ML model
  • Use a test ML model to perform a prediction based on new input data

Lab Prerequisites

You should be familiar with the following:

  • Connecting to an EC2 instance
  • Basic file edits and working from the Linux command-line
  • Python basics (or similar scripting language)
  • Conceptual understanding of Amazon Software Development Kit (SDK) and Application Programming Interface (API) is helpful, but not required

 

Lab Environment

Before completing the lab instructions the environment will look as follows:

  • A running EC2 instance
  • Note: This lab is serverless. The EC2 instance is started and configured for you by Cloud Academy simply for convenience sake. 

 

After completing the lab instructions the environment should look similar to:

About the Author

Alex is an Italian Software Engineer with a great passion for web technologies and music.

He spent the last 5 years building web products and deepening his knowledge on full stack web development and software design, with a main focus on frontend and UX.

Despite being a passionate coder, Alex worked hard on his software and sound engineering background, which provides him the tools to deal with multimedia, signal processing, machine learning, AI and many interesting topics related to math and data science.

Indeed, he had the opportunity to study and live in a very young and motivating environment in Bologna and Milan, two of the biggest and oldest Italian Universities. These experiences lead him to work on several projects involving robotics, machine intelligence, music semantic analysis and modern web development.

Alex is currently a Senior Software Engineer at Cloud Academy, a position that gave him the possibility to discover the Cloud world and exploit its potential as a web developer and data scientist.