ChatGPT’s free conversational interface offers a tantalizing glimpse into the future of AI.

ChatGPT is what everyone is talking about nowadays. Would it take all the jobs?

Or how to get better at hacking? Reading code is a hard skill to inculcate.

Or How to use the export command Linux shell has become a constant part of every ML Engineer, Data Scientist and Programmer’s life.

Parallelism and concurrency aren’t the same things. In some cases, concurrency is much more powerful.

In my last series of posts on Transformers, I talked about how a transformer works and how to implement one yourself for a translation task.

Finally, my program is running! Should I go and get a coffee?

Algorithms are an integral part of data science. While most of us data scientists don’t take a proper algorithms course while studying, they are important all the same.

In one of my previous posts, I talked about how to become a data Scientist using some awesome resources from Coursera .

ROC curves, or receiver operating characteristic curves, are one of the most common evaluation metrics for checking a classification model’s performance.

Python in many ways has made our life easier when it comes to programming.

Object-Oriented Programming or OOP can be a tough concept to understand for beginners.

I love working with shell commands. They are fast and provide a ton of flexibility to do ad-hoc things.

It was August last year and I was in the process of giving interviews.

Out of all spheres of technological advancement, artificial intelligence has always attracted the most attention from the general public.

Transformers have become the defacto standard for NLP tasks nowadays. They started being used in NLP but they are now being used in Computer Vision and sometimes to generate music as well.

Recently, I was looking for a toy dataset for my new book’s chapter (you can subscribe to the updates here ) on instance segmentation.

Transformers have become the defacto standard for NLP tasks nowadays.

PyTorch has sort of became one of the de facto standards for creating Neural Networks now, and I love its interface.

Before I even begin this article, let me just say that I love iPython Notebooks, and Atom is not an alternative to Jupyter in any way.

Most of us in data science has seen a lot of AI-generated people in recent times, whether it be in papers, blogs, or videos.

With ML Engineer job roles in all the vogue and a lot of people preparing for them, I get asked a lot of times by my readers to recommend courses for the ML engineer roles particularly and not for the Data Science roles.

Pandas is one of the best data manipulation libraries in recent times.

As Alexander Pope said, to err is human. By that metric, who is more human than us data scientists?

Creating my workstation has been a dream for me, if nothing else.

Data Exploration is a key part of Data Science. And does it take long?

Just recently, I had written a simple tutorial on FastAPI, which was about simplifying and understanding how APIs work, and creating a simple API using the framework.

Creating my own workstation has been a dream for me if nothing else.

Have you ever been in a situation where you want to provide your model predictions to a frontend developer without them having access to model related code?

Have you ever wondered how Facebook takes care of the abusive and inappropriate images shared by some of its users?

With the advent of so many computing and serving frameworks, it is getting stressful day by day for the developers to put a model into production .

Big Data has become synonymous with Data engineering. But the line between Data Engineering and Data scientists is blurring day by day.

Every few years, some academic and professional field gets a lot of cachet in the popular imagination.

Recently, I was reading Rolf Dobell’s The Art of Thinking Clearly, which made me think about cognitive biases in a way I never had before.

I have found myself creating a Deep Learning Machine time and time again whenever I start a new project.

Python provides us with many styles of coding. And with time, Python has regularly come up with new coding standards and tools that adhere even more to the coding standards in the Zen of Python.

Have you ever thought about how toxic comments get flagged automatically on platforms like Quora or Reddit?

It seems that the way that I consume information has changed a lot.

With Coronavirus on the prowl, there has been a huge demand across the world for MOOCs as schools and universities continue to shut down.

Feeling Helpless? I know I am. With the whole shutdown situation, what I thought was once a paradise for my introvert self doesn’t look so good when it is actually happening.

I know — Spark is sometimes frustrating to work with.

Many of my followers ask me — How difficult is it to get a job in the Data Science field?

Too much data is getting generated day by day. Although sometimes we can manage our big data using tools like Rapids or Parallelization , Spark is an excellent tool to have in your repertoire if you are working with Terabytes of data.

Have you ever been frustrated by doing data exploration and manipulation with Pandas?

XGBoost is one of the most used libraries fora data science.

A Machine Learning project is never really complete if we don’t have a good way to showcase it.

Recently I was working on tuning hyperparameters for a huge Machine Learning model.

A Machine Learning project is never really complete if we don’t have a good way to showcase it.

Data manipulation is a breeze with pandas, and it has become such a standard for it that a lot of parallelization libraries like Rapids and Dask are being created in line with Pandas syntax.

Recently, I got asked about how to explain confidence intervals in simple terms to a layperson.

A Data Scientist who doesn’t know SQL is not worth his salt

We as data scientists have got laptops with quad-core, octa-core, turbo-boost.

Algorithms and data structures are an integral part of data science.

Algorithms and data structures are an integral part of data science.

Algorithms and data structures are an integral part of data science.

Have you ever faced an issue where you have such a small sample for the positive class in your dataset that the model is unable to learn?

Collecting and analysing data, including but not limited to text, images, and video formats, is a huge part of various industries.

Time series prediction problems are pretty frequent in the retail domain.

Creating a great machine learning system is an art.

People ask me a lot about how to land a data science job?

Algorithms are an integral part of data science. While most of us data scientists don’t take a proper algorithms course while studying, they are important all the same.

A Machine Learning project is never really complete if we don’t have a good way to showcase it.

I like deep learning a lot but Object Detection is something that doesn’t come easily to me.

Data Science is the study of algorithms. I grapple through with many algorithms on a day to day basis, so I thought of listing some of the most common and most used algorithms one will end up using in this new DS Algorithm series .

Decision Trees are great and are useful for a variety of tasks.

Recently, I got asked about how to explain p-values in simple terms to a layperson.

Explain Like I am 5. It is the basic tenets of learning for me where I try to distill any concept in a more palatable form.

What do we want to optimize for? Most of the businesses fail to answer this simple question.

We, as data scientists have gotten quite comfortable with Pandas or SQL or any other relational database.

When we create our machine learning models, a common task that falls on us is how to tune them.

Creating a great machine learning system is an art. There are a lot of things to consider while building a great machine learning system.

I always get confused whenever someone talks about generative vs. discriminative classification models.

One of the main tasks while working with text data is to create a lot of text-based features.

I am a Mechanical engineer by education. And I started my career with a core job in the steel industry.

Data Science is the study of algorithms. I grapple through with many algorithms on a day to day basis, so I thought of listing some of the most common and most used algorithms one will end up using in this new DS Algorithm series .

Data Science is the study of algorithms. I grapple through with many algorithms on a day to day basis so I thought of listing some of the most common and most used algorithms one will end up using in this new DS Algorithm series.

Exploration and Exploitation play a key role in any business.

Pandas is a vast library. Data manipulation is a breeze with pandas, and it has become such a standard for it that a lot of parallelization libraries like Rapids and Dask are being created in line with Pandas syntax.

Big Data has become synonymous with Data engineering. But the line between Data Engineering and Data scientists is blurring day by day.

I love Jupyter notebooks and the power they provide.

I bet most of us have seen a lot of AI-generated people faces in recent times, be it in papers or blogs.

Good Features are the backbone of any machine learning model.

Python has a lot of constructs that are reasonably easy to learn and use in our code.

I distinctly remember the time when Seaborn came. I was really so fed up with Matplotlib.

Parallelization is awesome. We data scientists have got laptops with quad-core, octa-core, turbo-boost.

Python provides us with many styles of coding. In a way, it is pretty inclusive.

Learning a language is easy. Whenever I start with a new language, I focus on a few things in below order, and it is a breeze to get started with writing code in any language.

Visualizations are awesome. However, a good visualization is annoyingly hard to make.

Just Kidding, Nothing is hotter than Jennifer Lawrence. But as you are here, let’s proceed.

This post is the fourth post of the NLP Text classification series.

This post is the third post of the NLP Text classification series.

Kaggle is an excellent place for learning. And I learned a lot of things from the recently concluded competition on Quora Insincere questions classification in which I got a rank of 182/4037.

This is the second post of the NLP Text classification series.

Recently, I started up with an NLP competition on Kaggle called Quora Question insincerity challenge.

Recently I started up with a competition on kaggle on text classification, and as a part of the competition, I had to somehow move to Pytorch to get deterministic results.

With the problem of Image Classification is more or less solved by Deep learning, Text Classification is the next new developing theme in deep learning.

Graphs provide us with a very useful data structure. They can help us to find structure within our data.

We all know about the image classification problem. Given an image can you find out the class the image belongs to?

Recently Kaggle master Kazanova along with some of his friends released a “How to win a data science competition” Coursera course.

Distributions play an important role in the life of every Statistician.

Newton once said that “God does not play dice with the universe”.

Recently Quora put out a Question similarity competition on Kaggle. This is the first time I was attempting an NLP problem so a lot to learn.

I have been looking to create this list for a while now.

Today we will look into the basics of linear regression. Here we go :

A data scientist needs to be Critical and always on a lookout of something that misses others.

As a data scientist I believe that a lot of work has to be done before Classification/Regression/Clustering methods are applied to the data you get.

This is a post which deviates from my pattern fo blogs that I have wrote till now but I found that Finance also uses up a lot of Statistics.

It has been quite a few days I have been working with Pandas and apparently I feel I have gotten quite good at it.

It has been a long time since I wrote anything on my blog.

Yesterday I got introduced to awk programming on the shell and is it cool.

Shell Commands are powerful. And life would be like hell without shell is how I like to say it(And that is probably the reason that I dislike windows).

When it comes to data preparation and getting acquainted with data, the one step we normally skip is the data visualization.

I generally have a use case for Hadoop in my daily job.

Last time I wrote an article on MCMC and how they could be useful.

The things that I find hard to understand push me to my limits.

I have been using Hadoop a lot now a days and thought about writing some of the novel techniques that a user could use to get the most out of the Hadoop Ecosystem.

In online advertising, click-through rate (CTR) is a very important metric for evaluating ad performance.

This is a simple illustration of using Pattern Module to scrape web data using Python.

THE PROBLEM: Recently I was working on the Criteo Advertising Competition on Kaggle.

This is part one of a learning series of pyspark, which is a python binding to the spark program written in Scala.

It has been some time since I was stalling learning Hadoop.