Time Series for H2O with Modeltime

I share some notes from an online community meetup on doing Time Series in H2O-3 with the Modeltime R package. The new R Package is neat, I hope that someone builds something like that for Python!
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H2O Wave App Tutorials

Wave is a new Open Source pythonic framework for build applications. It’s fast (written in Go) and let’s you focus only on your code without worrying about CSS, Javascript, and other HTML related stuff. I’m starting an H2O Wave App Tutorial post.
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Install H2O’s Wave on AWS Lightsail or EC2

How to set up Wave, H2O’s new open source python based framework, that let’s you build apps. I share a tutorial on how to setup Wave on AWS’s Lightsail or EC2 instances.
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Isolation Forests in H2O.ai

H2O.ai releases Isolation Forests for open source, an anomaly detection method.
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MLI with RSparkling

How to do Machine Learning Interpretability with RSparkling
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Interpreting Machine Learning Models

A short introduction on Machine Learning Interpretability (MLI). Learn the basics of MLI.
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Making AI Happen Without Getting Fired

I share my notes from this must watch if you’re thinking of putting AI models into production.
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Feature Engineering in Driverless AI

Learn about the complex feature engineering that Driverless AI does. How to squeeze more performance out of your machine learning models.
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What’s new in Driverless AI?

New updates to Driverless AI keep pushing the innovation envelope. Learn about key differentiators.
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Open Source

I’ve been think a lot about open source lately. I’ve also been thinking of closed source and open core too.
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Machine Learning Interpretability in R

In this video the presenter goes over a new R package called ‘iML.’ This package has a lot of power when explaining global and local feature importance. These explanations are critical, especially in the health field and if your under GDPR regulations. Now, with the combination of Shapley, LIME, and partial dependence plots, you can figure out how the model works and why. I think we’ll see a lot of innovation in the ‘model interpretation’ space going forward.
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Matrix Factorization for Missing Values

I stumbled across an interested reddit post about using matrix factorization (MF) for imputing missing values. The original poster was trying to solve a complex time series that had missing values. The solution was to use matrix factorization to impute those missing values. Since I never heard of that application before, I got curious and searched the web for information. I came across this post using matrix factorization and Python to impute missing values.
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MLI Using LIME Framework

Understanding the LIME framework for Machine Learning Interpretability.
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Exploring H2O.ai

A few years ago RapidMiner incorporated a fantastic open source library from H2O.ai. That gave the platform Deep Learning, GLM, and a GBT algos, something they were lacking for a long time. If you were to look at my usage statistics, I’d bet you’d see that the Deep Learning and GLM algos are my favorites. Just late last year H20.ai released their driverless.ai platform, an automated modeling platform that can scale easily to GPUs.
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Guide to Getting Started in Data Science

My experiences in learning Data Science and working at a Startup. I share my lessons learned and tips on how you can get started in the field of Data Science.
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Newsletter

Sign up for the Neural Market Trends Newsletter. Get valuable links and posts from the Data Science and Machine Learning sphere!
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Introduction to Deep Learning

A great introduction to Deep Learning, Keras, and installing it on RapidMiner. A video and my notes.
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Latest Writings Elsewhere

Where my Machine Learning articles have been published. A running list when I remember to update it.
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