Algorithms Interviews

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. Many companies ask data structures and algorithms as part of their interview process for hiring data scientists.

Now the question that many people ask here is what is the use of asking a data scientist such questions. The way I like to describe it is that a data structure question may be thought of as a coding aptitude test.

We all have given aptitude tests at various stages of our life, and…

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.

In this post, I will go a step further and try to explain BERT, one of the most popular NLP models that utilize a Transformer at its core and which achieved State of the Art performance on many NLP tasks including Classification, Question Answering, and NER Tagging when it was first introduced.

Specifically, unlike other posts on the same topic, I will try to go through the highly influential BERT paper — Pre-training of Deep…

In my last post, I talked about Object-Oriented Programming(OOP). And I specifically talked about a single magic method __init__ which is also called as a constructor method in the OOP terminology.

The magic part of __init__ is that it gets called whenever an object is created automatically. But it is not the only one in any sense. Python provides us with many other magic methods that we end up using without even knowing about them. Ever used len(), print() or the [] operator on a list? You have been using dunder methods.

In this post, I would talk about five…

ROC curves, or receiver operating characteristic curves, are one of the most common evaluation metrics for checking a classification model’s performance. Unfortunately, many data scientists often just end up seeing the ROC curves and then quoting an AUC (short for the area under the ROC curve) value without really understanding what the AUC value means and how they can use them more effectively.

Other times, they don’t understand the various problems that ROC curves solve and the multiple properties of AUC like threshold invariance and scale invariance, which necessarily means that the AUC metric doesn’t depend on the chosen threshold…

TLDR; Do let me know in the comments or to my mail ID: rahul@mlwhiz.com, if you want to contribute articles on ML or Data Science. I would add you as an author.

Full Disclosure here: As part of the editing, I would try to provide you with comments and editing guidelines on your articles. I might also do some hyperlinking to other articles and add some links to my site and some courses as well. I might also add some blogs on my original site as well(mlwhiz.com).

A note on minimal formatting requirements for a post, apart from good content:

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

With those heavy steel enforced gumboots and that plastic helmet, venturing around big blast furnaces and rolling mills. Artificial safety measures, to say the least, as I knew that nothing would save me if something untoward happens. Maybe some running shoes would have helped. As for the helmet. I would just say that molten steel burns at 1370 degrees C.

As I realized based on my constant fear, that job was not for me, and so I made it my…

Machine learning is a growing field getting a lot of attention, but getting machine learning jobs is still very difficult. Landing an engineering role at a big company means knowing not just Data Science, but also things like programming and system design. More often than not, there’s a lot of research and learning involved to prepare for applying for a new position.

When I was preparing for my machine learning job interviews, I started preparing two months prior to the interviews. That’s when I really understood what I needed for the data science and machine learning positions I wanted. …

PYTHON SHORTS

Object-Oriented Programming or OOP can be a tough concept to understand for beginners. And that’s mainly because it is not really explained in the right way in a lot of places. Normally a lot of books start by explaining OOP by talking about the three big terms — Encapsulation, Inheritance and Polymorphism. But the time the book can explain these topics, anyone who is just starting would already feel lost.

So, I thought of making the concept a little easier for fellow programmers, Data Scientists and Pythonistas. The way I intend to do is by removing all the Jargon and…