Apr 23 · 14 min read · From a 200-Row Dataset to a Deployed ML-Powered Platform — A Complete Developer Journey Introduction Have you ever reported a pothole to your city and never heard back? Or seen overflowing garbage bin
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Oct 7, 2025 · 4 min read · How Search Engines Understand Text: The Magic Behind TF-IDF Have you ever searched for something on Google and instantly got the most relevant results?You type a few words… and in milliseconds, it finds exactly what you were thinking. But how does it...
Join discussionAug 9, 2025 · 6 min read · In the vast universe of Natural Language Processing (NLP), amidst the rise of complex transformer architectures, some foundational algorithms remain indispensable. TF-IDF is one such pillar. It's a technique that allows us to numerically represent te...
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May 1, 2025 · 1 min read · For this next installment in our series demystifying AI concepts, we delve into how the meaning and importance of words within a document can be numerically captured. Bag-of-Words (BoW) offers a straightforward approach by representing a text as the ...
Join discussionApr 15, 2025 · 3 min read · So during one of my early NLP experiments, I came across a massive dataset of Amazon product reviews, and thought — can I train a model to automatically detect whether a review is positive, negative, or neutral? I knew that many people had done senti...
Join discussionApr 13, 2025 · 8 min read · 📌 What is Text Vectorization? In NLP, machines can’t understand text directly — they understand numbers. Text vectorization is the process of converting textual data into numerical vectors so that we can feed them into machine learning or deep learn...
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Nov 23, 2024 · 6 min read · Introduction What is Feature Extraction from text? To text representation To text recognition Why do we need it? Why is it difficult? What is the core idea? What are the techniques? OHE (One Hot Encoding) BOW (Bag of Words) ngrams TfIdf ...
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