Development Data Science Data Modeling

Finding Actionable Insights using Keras Autoencoders

Using Autoencoders to Better Understand your Customers - Measuring Customer Credit Risk
Rating: 4.1 out of 5 (24 ratings)
1,731 students
Created by Manuel Amunategui
Enroll now
Finding Actionable Insights using Keras Autoencoders
Rating: 4.1 out of 5 (24 ratings)
1,731 students
Buy now
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

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
Course content
1 section • 7 lectures • 56m total length
  • Introduction
    01:08
  • About me
    01:10
  • What is an Autoencoder and what is it good for?
    06:16
  • Preparing the Open Source Statlog - German Credit Data
    11:07
  • Quick classification look with AutoML
    05:28
  • Building our Keras Autoencoder
    19:14
  • Investigating anomalies
    11:52

Instructor
Manuel Amunategui
Data Scientist & Quantitative Developer
Manuel Amunategui
  • 4.4 Instructor Rating
  • 1,010 Reviews
  • 29,055 Students
  • 12 Courses

Data scientist with over 20-years experience in the tech industry, MAs in Predictive Analytics and International Administration, author of Monetizing Machine Learning and The Little Book of Fundamental Indicators, founder of FastML, reached top 1% on Kaggle and awarded "Competitions Expert" title, taught over 20,000 students on Udemy and VP of Data Science at SpringML.


From consulting in machine learning, healthcare modeling, 6 years on Wall Street in the financial industry, and 4 years at Microsoft, I feel like I’ve seen it all. And this has opened my eyes to the huge gap in educational material on applied data science. Like I say:


"It just ain’t real 'til it reaches your customer’s plate"


I am a startup advisor and available for speaking engagements with companies and schools on topics around building and motivating data science teams, and all things applied to machine learning.


Reach me at amunategui@gmail.com