The First Online Datathon
This February Data Science Society will make the difference with the First Online Data Challenge available for everyone! The 48-hours Data Hackathon […]
How to Build an Email Sentiment Analysis Bot: An NLP Tutorial
Today, with machine learning and large amounts of data harvested from social media and review sites, we can train models to identify the sentiment of a natural language passage with fair accuracy.
In this tutorial, you will learn how you can build a bot that can analyze the sentiment of emails that it receives and notify you about emails that may require your attention immediately.
A Deep Learning Tutorial: From Perceptrons to Deep Networks
In this tutorial, I’ll introduce you to the key concepts and algorithms behind deep learning, beginning with the simplest unit of composition and building to the concepts of machine learning in Java.
Workshop – Probabilistic Programming for Machine Learning
Probabilistic Programming for Machine Learning Recently, the plethora of deep learning methods have achieved unequivocal success on various tasks, however all of […]
28.11.2017 meetup – Reveal hidden patterns in public data
This November we spoke about NLP and Text Mining techniques used in working with open Government data in a practical way. The topic […]
Datathon winner – Revealing hidden links through open data
On 24 to 26 of March Data Science Society organized a Datathon – the first-of-its-kind data analysis competition in Central and Eastern […]
Data Talk & Beer: Powered by data at VMware
It is time to have a nice talk about data science powered by a couple of beers. DATA Talk & Beer held […]
How Google Searches and How Amazon Recommends
Maria explained, information retrieval is mostly about finding text documents from within large collections by predetermined criteria – exactly the task that Google Search does. In order to find documents efficiently, you need to preprocess them.
A Few Tricks in the Bag of a Credit Risk Modeler
I revealed solutions you can’t find in any popular textbook that I have applied for problems faced while developing credit risk models. Credit risk models predict whether a borrower will pay their loan back, and people like me in effect decide if you will be approved for a credit card or a mortgage loan. Statistical classification algorithms work best for such problems with a binary outcome. In particular, the logistic regression is preferred in practice due to its quantification of a probability of default between 0 and 1.
Launching regular meetups and update about topic selection
Data Science Society have decided to launch one new initiative and postpone the topic selection campaign. Regular casual industry events are introduced, […]
Why Custom Software Solutions Are Critical for Industry Success
Technology has become a cornerstone of success in the modern business landscape. It is vital for streamlining operations, enhancing customer experiences, and […]