Features of Python that I had no idea even existed

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These days I have a newfound pastime — reading Python documentation just for fun! When you read something at leisure, you tend to notice interesting tidbits that you would have missed otherwise. So, here is the list of tidbits that made me go “Oh! You can do that in Python?”

1. Function Attributes

Similar to how you can set attributes of classes and objects, you can also set attributes to functions.

We have set the attributes ‘optional_return’ in line 10 and ‘is_awesome’ in line 11. We have accessed those attributes outside of the function later in lines 19 & 20. …

Avoid these mistakes that I observed from 100+ interviews

Over the course of the last two years, I have interviewed a lot of candidates for entry-level data science roles. I noticed a few completely avoidable mistakes being repeated frequently by many candidates.

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Here are some of those mistakes and ways to avoid them.

#1: Diving way too deep, way too early

Too often I see the following conversation:

Can you tell me a little bit about XYZ project on your resume?

“OK, so we start by converting PDF to images using pdf2image library and then run Tesseract OCR on it to convert the image into text. …

New research explained

Precision, recall, and F-score won’t get you far. Use these new insightful metrics instead.

Interpretable and fine-grained evaluation techniques are crucial for making well-grounded improvements in any ML model. Photo by Kent Pilcher on Unsplash


NER is typically approached as a sequence labelling problem and the models for it are usually evaluated through traditional classification metrics such as precision, recall, F-score, etc. You can read a primer on the topic in this post.

What is NER and why should you care?

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What is Named Entity Recognition (NER)?

It is the process of identifying proper nouns from a piece of text and classifying them into appropriate categories. These categories can be generic like ‘Organization’, ‘Person’, ‘Location’, etc. or they can be tailor-made for a particular application, e.g. ‘Programming Language’, ‘Blogging site’, etc.

In simpler words, if you want to find out ‘who’, ‘what’, ‘when’, ‘where’ from a sentence, then NER is your task.

Take the following example (or try it yourself here):

Charudatta Manwatkar

Physics graduate. Data Scientist. Nerd. जे जे आपणासी ठावे। ते ते इतरांसी सांगावे।

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