In a previous blog post
I demonstrated how the vector datatype in Elasticsearch can be used to search words by their semantic meaning.
In this post I will show how a reverse image search for paintings can be implemented using the same methods.
Given a photo of a painting, we will use Elasticsearch to find other paintings which look similar.
You heard all the potentials that Machine Learning (ML) is capable.
Detecting fraud, predicting machine failure or understanding customer behavior.
ML delivers tremendous impact to a variety of businesses.
Data scientists often find themselves spending a lot of time with data acquisition and preparation,
yet most tutorials start with ready to use datasets.
This time we will start with nothing but a simple problem and gather the data with scrapy to provide insight into the
process from data gathering to model creation.
Text classification is a common task where machine learning is applied.
Be it questions on a Q&A platform, a support request, an insurance claim or a business inquiry - all of these are usually written in free
form text and use vocabulary which might be specific to a certain field.
This article demonstrates how such classification problems can be tackled with the open source neural network library Keras.
One of the goals I set for this year is to explore Machine Learning (ML), so after having done a couple of courses here and there, I decided to do a -rather simple- starting project, where I could deal with some of the basic stages of the ML: Get the data, prepare it, choose a model, train it, evaluate it, export it, and make the predictions available for use.
For this first project, I chose: