AWS Machine Learning in Motion
Kesha Williams
  • Course duration: 1h 43m
    Estimated full duration: 7h
  • MEAP began May 2018
  • Publication in July 2018 (estimated)
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.
Table of Contents detailed table of contents

Course Introduction

2054 and Machine Learning

Course Overview

What is Suspicious Activity Monitor (SAM)?

Cost of Running AWS Machine Learning Models

Machine Learning Overview

What is Machine Learning?

What Machine Learning is Not

General Uses of Machine Learning

Supervised Learning

Unsupervised Learning

Reinforcement Learning

Quick Deep Learning

Problem Identification and Data Collection

Predictive Policing

Inspiration Behind SAM

AWS Architecture Diagram

Artificial Intelligence and AWS Technologies

Machine Learning with AWS Lambda, Python, and Twitter

Data Collection

Data Review

AWS QuickSight Overview

Load Data

Visualize and Understand the Data

Model the Data

AWS Machine Learning Overview

Prepare Data

Load Data to AWS S3

Train the Model

Evaluate the Model

Optimize the Model

Test the Model

Generate and Use Predictions

Enable Model API Endpoint

Use the Model API Endpoint from AWS Lambda

Maintaining the Model

Identify New Data Points

Retrain the Model

AWS Internet of Things

AWS IoT Overview

AWS IoT Button Architecture Diagram

Using AWS IoT to trigger AWS Lambda

The Future of Machine Learning

Social Impact

Machine Bias

SAM 2.0

Course Recap

Next Steps

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!

Prerequisites

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. In addition to being a software engineer, she 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. (http://www.iamsam.tech/) which can predict the likelihood of crime in a certain area using machine learning.

Manning Early Access Program (MEAP) Watch raw videos as they are added, and get the entire course, complete with transcript and exercises, when it is finished.
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