Category: AI/ML Research

Pandas , NumPy , and Scikit-learn .

Imbalanced datasets, where a majority of the data samples belong to one class and the remaining minority belong to others, are not that rare.

You’ve trained your machine learning model, and it’s performing great on test data.

There’s no doubt that search is one of the most fundamental problems in computing.

The rise of language models, and more specifically large language models (LLMs), has been of such a magnitude that it has permeated every aspect of modern AI applications — from chatbots and search engines to enterprise automation and coding assistants.

Missing values appear more often than not in many real-world datasets.

I must say, with the ongoing hype around machine learning, a lot of people jump straight to the application side without really understanding how things work behind the scenes.

Machine learning is not just about building models.

Machine learning workflows typically involve plenty of numerical computations in the form of mathematical and algebraic operations upon data stored as large vectors, matrices, or even tensors — matrix counterparts with three or more dimensions.

Feature engineering is a key process in most data analysis workflows, especially when constructing machine learning models.