Finding Actionable Insights using Keras Autoencoders

Name: Finding Actionable Insights using Keras Autoencoders
Rating: 4.6 (89 reviews)

Using Autoencoders to Better Understand your Customers - Measuring Customer Credit Risk

Created byManuel Amunategui

Last updated 4/2020

English

What you'll learn

Learn to build a Keras Autoencoder using Python
Learn to extract actionable insights from data using unsupervised and semi-supervised modeling
Learn to find anomalies in data

Course content

1 section • 7 lectures • 56m total length

Introduction1:08
Find source code here:
https://www.viralml.com/video-content.html?v=2UueN6lI62o
About me1:10
What is an Autoencoder and what is it good for?6:16
Preparing the Open Source Statlog - German Credit Data11:07
Quick classification look with AutoML5:28
Building our Keras Autoencoder19:14
Investigating anomalies11:52

Requirements

You should know a little Python, how to install Python libraries, and how to use Jupyter notebook

Description

Please join me for another exciting data science class where we apply autoencoders or unsupervised learning towards the pursuit of knowledge.

Remember at the end of the day modeling and data science don't mean much if we can't extract actual insights to help guide our customers, our friends, the research community in the advancement of whatever it is they are after using data. Autoencoders can help you better understand your data, answer your questions, and even discover new ones! Please join me on this exciting adventure!

Who this course is for:

Anybody wanting to analyze data
Anybody wanting to perform anomaly detection
Anybody interested in Autoencoders and machine learning with Keras