AWS Machine Learning in Motion
Kesha Williams
  • Course duration: 3h 42m
    28 exercises

Good course for people looking to build an application quickly, with minimal amount of ML coding. The concept is excellent!

Tanya Dixit
You can use machine learning for data analysis without mastering a lot of complex math, frameworks, and coding techniques. Machine learning services hosted on the Amazon Web Services cloud platform are a perfect way to get started even if you've never built a machine learning model before. This interactive liveVideo course gives you a crash course in using AWS for machine learning, teaching you how to build a fully-working predictive algorithm.

About the subject

AWS offers a suite of tools perfect for training and deploying your own ML models. Better yet, it comes with all the benefits developers love about AWS, with helpful support at your fingertips, security for every step, and the ability to pay for just what you need! It's perfect for developers who want to get the job done with minimal fuss.

About the video

See it. Do it. Learn it! This amazing liveVideo course will put your machine learning on the fast track! AWS Machine Learning in Motion gives you a complete tour of the essential tools, techniques, and concepts you need to do complex predictions and other data analysis using the AWS machine learning services!

In this interactive liveVideo course, you'll get started with cloud-based machine learning under the guidance of experienced software engineer and TED Speaker Kesha Williams. You'll cut through the theory and jargon as you build a working crime-fighting machine learning algorithm! Starting with a tour of AWS' tools and the basics of machine learning, you'll dive into the learning algorithms supported by AWS, such as linear regression, multinomial logistic regression, and logistic regression.

Then comes the really fun part! You'll get your hands dirty as you obtain and prepare a data set for your own machine learning model. You'll learn how to train the model to recognize patterns and optimize it to become more accurate. With engaging exercises throughout, you'll also practice with the AWS Lambda, Boto3, Quicksight, and IoT tools to build a completely serverless application that can make decisions based on real-time predictions!

You'll start to feel like Tom Cruise in Minority Report as your algorithm grows into something that can look at images and tell you if a crime is happening! With some basic Python skills and AWS Machine Learning in Motion, you can build it right now!

Table of Contents detailed table of contents

Course Introduction

Welcome! Navigating in your course

2054 and Machine Learning

Course Overview

What is Suspicious Activity Monitor (SAM)?

Cost of Running AWS Machine Learning Models

First activity

Machine Learning Overview

What is Machine Learning?

A short exercise

What Machine Learning is Not

A short exercise

General Uses of Machine Learning

Read more about Machine Learning

Supervised Learning

A short exercise

Unsupervised Learning

A short exercise

Reinforcement Learning

A short exercise

Quick Deep Learning

More reading on Machine Learning and Deep Learning

Optional Reading

Problem Identification and Data Collection

Predictive Policing

Inspiration Behind SAM


AWS Architecture Diagram

A short exercise

Artificial intelligence and AWS technologies


Machine Learning with AWS Lambda, Python, and Twitter

Data collection

Reading material about data collection

A short exercise


Data Review

AWS QuickSight overview


Load data


Visualize and understand the data


Model the Data

AWS Machine Learning overview

Create data source

Short quiz


Analyze data

Short quiz


Optional Reading: Data analysis example, NYC Taxi and fare information

Train the model

Optional Reading: Stochastic Gradient Descent


Evaluate the model

Short quiz


Optional: Read more about evaluating models

Optimize the model


Test the model

Short quiz


Generate and Use Predictions

Enable model API endpoint


Use the Model API Endpoint from AWS Lambda

Optional Reading - Python basics


Optional Reading - AWS Lambda basics

Maintaining the Model

Identify new data points


Retrain the model

Short quiz


AWS Internet of Things

AWS IoT and Machine Learning overview

Bonus Activity

Bonus reading on the Internet of Things

The Future of Machine Learning

Social impact

Machine bias

SAM 2.0

Course recap

Next steps

We welcome your feedback! 2 minute course survey



Requires a beginner-level understanding of AWS and the Python programming language. No experience with machine learning is needed.

What you will learn

  • Machine learning in the cloud
  • Combining AWS Lambda and Python
  • Reviewing data with AWS QuickSight
  • Loading your data into AWS S3 for modelling
  • Generating and using predictions
  • Retraining ML models

About the instructor

Kesha Williams is a software engineer with over 20 years' experience specializing in application development using Java, Angular, and Amazon Web Services. Also honored as an Amazon Alexa Champion, she has developed several popular Alexa skills and speaks is the Alexa conference 2019 keynote speaker. Kesha trains and mentors thousands of software engineers in the US, Europe, and Asia while teaching technology courses at the university level. She also developed S.A.M. ( which can predict the likelihood of crime in a certain area using machine learning. Follow Kesha on Twitter @KeshaWillz.

We interviewed Kesha as a part of our Six Questions series. Check it out here.

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Shows capabilities of AWS Machine Learning as well as other AWS services in context of a meaningful real-life application. Very approachable.

Anonymous reviewer

Ideal for people who are just getting started with machine learning.

Kalyan Reddy Kasireddy, MCT, MCSA

Excellent tutorial on trying out and learning about AWS Machine Learning.

Satej Kumar Sahu, Senior Software Developer, Ellucian