Why should you read a 25min reading time medium post? Well, here I tried to condense the complete path of a machine learning project, from data analysis to deployment on AWS EC2.
In the age of innocence, after following our first ML courses, we all thought that to be a data scientist, working on notebooks would have been enough.
Once we left kindergarten, we learnt that this is far from the truth.
Nowadays there is plenty of other skills a data scientist must have other than knowledge of machine learning algorithms (or more often library usage).
This post aims to…
One nice day an idea came to your mind: I can make big money by selling a machine learning model to a company that might be interested in it.
You like the idea, and you decide you want to address a company with a press department and sell a text classifier. You feel excited.
However, at this stage, reality strikes you hard: “I am a data scientist, I can build a great classifier, but when I show my code at managers, they look at it as I would look at hieroglyphs”.
The aim of this post is to describe how one can leverage a deep learning framework to create a hybrid recommender system i.e. a model exploiting both content and collaborative-filter data. The idea is to tackle issues in two different steps: first collaborative filtering and content based model separately, then a combination of the two, to get better results.
Before diving into recommender system, we have been recommended to introduce ourself.