How to codify knowledge as sequence of numbers

Photo by Mick Haupt on Unsplash

In the first part of the series, we discussed how feature crosses can be used to combine multiple features to extract a non-linear pattern in the data. Now, we will continue our journey by exploring the concept of representing a feature with embedding and how it is used in many fields like NLP and Computer Vision.

Intuition of Embedding

Imagine your little brother just graduated from high school and about to enter university. Like a good older sibling you want to give him a brand-new laptop as a gift so your brother could use it for doing assignments or other uni stuff. …

How to do feature engineering beyond scaling and one-hot encoding

Photo by Annie Spratt on Unsplash


Being a data scientist is like being a craftsman. You are equipped with a set of tools and required to create something beautiful yet functional out of simple material. Some of your work might be done by automatic machinery, but you know that your true power is creativity and intricate skill to work by hand.

In this series, we will hone your skillset by exploring several approaches to represent data as a feature. These approaches could improve the learnability of your model especially if you have tons of data in hand.

Feature Cross

Imagine that you have data with the following pattern…

Rezatama Fathullah

MS Data Science at the University of Melbourne

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