“Visualization gives you answers to questions you didn’t know you had.” — Ben Shneiderman

My day-to-day work as a Data Scientist requires a great deal of experimentation. That means I rely a lot on data visualization to explore the dataset I’m working on.

And I couldn’t relate more to Ben Shneiderman’s quote! Data visualization gives me answers to questions I hadn’t even considered before. After all, a picture is worth a thousand data points!

This naturally leads to the million-dollar question — which Python library should you use for data visualization? There are quite a few across the board.


Convolutional neural networks (CNN) — the concept behind recent breakthroughs and developments in deep learning.

CNNs have broken the mold and ascended the throne to become the state-of-the-art computer vision technique. Among the different types of neural networks (others include recurrent neural networks (RNN), long short term memory (LSTM), artificial neural networks (ANN), etc.), CNNs are easily the most popular.

These convolutional neural network models are ubiquitous in the image data space. They work phenomenally well on computer vision tasks like image classification, object detection, image recognition, etc.

So — where can you practice your CNN skills? …

You have seen them, heard about them and probably have already used them for various tasks, without even realising what happens under the hood?

Yes, I’m talking about none other than — Regular Expressions; the quintessential skill for a data scientist’s tool kit!

They are used everywhere from data pre-processing to natural language processing, pattern matching, web scraping, data extraction and what not!

That’s why I wanted to write this article, to list some of those mundane tasks that you or your data team can automate with the help of Regular Expressions.

Table of Contents

  1. Extracting emails from a Text Document
  2. Regular Expressions…

The majority of breakthroughs and state-of-the-art frameworks we see are developed in the English language. I have long wondered if we could use that and build NLP applications in vernacular languages.

Here’s what we’ll cover in this article:

  1. What are the Languages of the Indian Subcontinent?
  2. Text Processing for Indian Languages using Python
  3. Trends in Multilingual NLP Research

What are the Languages of the Indian Subcontinent?

The Indian Subcontinent is a combination of many nations, here’s what Wikipedia says:

The Indian subcontinent is a term mainly used for the geographic region surrounded by the Indian Ocean: Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan and Sri Lanka.

These nations represent great diversity in languages, cultures, cuisines etc.

Fake news is a major concern in our society right now.

It has gone hand-in-hand with the rise of the data-driven era — not a coincidence when you consider the sheer volume of data we are generating every second!

So what role has Machine Learning played in this?

I’m sure you must have heard about a machine learning technique that generates fake videos mimicking famous personalities.

Similarly, Natural Language Processing (NLP ) techniques are being used to generate fake articles — a concept called “Neural Fake News”.

This aggravates the risk of them being exploited for spreading propaganda and chaos…


I love working in the Natural Language Processing (NLP) space. The last couple of years have been a goldmine for me — the level and quality of developments have been breathtaking.

But this comes with its own share of challenges. One of the biggest obstacles is to convert NLP techniques into practical code. This is where my appreciation for Apple’s Natural Language Toolkit — the library that is built on top of Core ML 3, really grows.

It makes life for a developer, NLP engineer or data scientist remarkably easy! …


Imagine the ability to build amazing applications by using State-of-the-Art machine learning models without having to know in-depth machine learning. Welcome to Apple’s Core ML 3!

Are you an avid Apple fan? Do you use the iPhone? Ever wondered how Apple uses machine learning and deep learning to power its applications and software?

If you answered yes to any of these questions — you’re in for a treat! Because in this article, we will be building an application for the iPhone using deep learning and Apple’s Core ML 3. Here’s a quick look at the app:

Software developers, programmers, and…

Python is widely considered the best and most effective language for data science.

But here’s the thing — data science is a vast and ever-evolving field. The languages we use to build our data science models have to evolve with it.

Remember when R was the go-to language? That was swiftly overtaken by Python. Julia also came up last year for data science and now there’s another language that is blossoming.

Yes, I’m talking about Swift for data science.

In this article, we will learn about Swift as a programming language and how it fits into the data science space…

Introduction to the World of BERT

It’s not an exaggeration to say that Google AI’s BERT has significantly altered the NLP landscape.

Imagine using a single model that is trained on a large unlabelled dataset to achieve State-of-the-Art results on 11 individual NLP tasks. And all of this with little fine-tuning. That’s BERT! It’s a tectonic shift in how we design NLP models.

BERT has inspired many recent NLP architectures, training approaches and language models, such as Google’s TransformerXL, OpenAI’s GPT-2, XLNet, ERNIE2.0, RoBERTa, etc.

Note: In this article, we are going to talk a lot about Transformers. If you aren’t familiar with it, feel free…

Have you heard about the latest Natural Language Processing framework that was released recently?

I don’t blame you if you’re still catching up with the superb StanfordNLP library or the PyTorch-Transformers framework!

There has been a remarkable rise in the amount of research and breakthroughs happening in NLP in the last couple of years.

I can trace this recent rise to one (seismic) paper — “Attention is All You Need” by Google AI in June 2017.

This breakthrough has spawned so many new and exciting NLP libraries that enable us to work with text in ways that were previously limited…

Mohd Sanad Zaki Rizvi

Technophile|Computer Science Afficionado| Recently into Data Science and ML

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