A Comprehensive Guide to Understand and Implement Text Classification in Python

One of the widely used natural language processing task in different business problems is “Text Classification”. The goal of text classification is to automatically classify the text documents into one or more defined categories. Some examples of text classification are:
• Understanding audience sentiment from social media,
• Detection of spam and non-spam emails,
• Auto tagging of customer queries, and
• Categorization of news articles into defined topics.

Humans-in-the-Loop? Which Humans? Which Loop?

For people working in Artificial Intelligence, the term “Human-in-the-Loop” is familiar i.e. a human in the process to validate and improve the AI. There are many situations where it applies, as many as there are AI applications. However. there are still some distinct different ways it can be deployed even within the same application.

Machine Learning is Fun Part 6: How to do Speech Recognition with Deep Learning

… But speech recognition has been around for decades, so why is it just now hitting the mainstream? The reason is that deep learning finally made speech recognition accurate enough to be useful outside of carefully controlled environments.

Text Classification using machine learning

The goal is to improve the category classification performance for a set of text posts. The evaluation metric is the macro F1 score.

Why Deep Learning is perfect for NLP (Natural Language Processing)

Deep learning brings multiple benefits in learning multiple levels of representation of natural language. Here we will cover the motivation of using deep learning and distributed representation for NLP, word embeddings and several methods to perform word embeddings, and applications.

NLP – Building a Question Answering model

I recently completed a course on NLP through Deep Learning (CS224N) at Stanford and loved the experience. Learnt a whole bunch of new things. For my final project I worked on a question answering model built on Stanford Question Answering Dataset (SQuAD). In this blog, I want to cover the main building blocks of a question answering model.

Packaging Shiny applications: A deep dive

This post is long overdue. The information contained herein has been built up over years of deploying and hosting Shiny apps, particularly in production environments, and mainly where those Shiny apps are very large and contain a lot of code.


Weird but (sometimes) useful charts