5, 10 or 20 seats+ for your team - learn more
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!
We interviewed Kesha as a part of our Six Questions series. Check it out here.
Good course for people looking to build an application quickly, with minimal amount of ML coding. The concept is excellent!
Shows capabilities of AWS Machine Learning as well as other AWS services in context of a meaningful real-life application. Very approachable.
Ideal for people who are just getting started with machine learning.
Excellent tutorial on trying out and learning about AWS Machine Learning.
geekle is based on a wordle clone.