Finding Actionable Insights using Keras Autoencoders
- You should know a little Python, how to install Python libraries, and how to use Jupyter notebook
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!
- Anybody wanting to analyze data
- Anybody wanting to perform anomaly detection
- Anybody interested in Autoencoders and machine learning with Keras
- About me01:10
- What is an Autoencoder and what is it good for?06:16
- Preparing the Open Source Statlog - German Credit Data11:07
- Quick classification look with AutoML05:28
- Building our Keras Autoencoder19:14
- Investigating anomalies11:52
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 firstname.lastname@example.org