In July I wrote I Checked My Own ASR Dataset for Leakage — Here’s What I Found. The short version: 41.6% of the test rows in my Malayalam speech corpus had an exact-duplicate transcript in the training split. Any number I computed on that test set was partly measuring memorisation. I filtered the test set
I Re-Tested My Malayalam ASR Model on Clean Data — Part 2 Read More »
Picture this. You spend two minutes training a spam classifier. It works beautifully. Then you close your notebook, and poof — it’s gone. Tomorrow, you retrain it again. And again. Sounds exhausting, right? That’s where model persistence with pickle and joblib comes in. This single idea saves you hours of repeated work, and it’s one
Model Persistence with Pickle and Joblib: Stop Retraining, Start Saving Read More »
Your inbox filters junk mail before you even see it. Your news feed quietly flags dubious headlines. Behind both features sits the same idea: spam and fake news detection through pattern recognition, not human review. In this article, you’ll build that idea from scratch, in plain language, with real Python code. This post is the
Spam and Fake News Detection with Machine Learning Read More »
Every day, people leave millions of reviews online. Some praise a product. Others complain about it. Reading all of them by hand is impossible. So, how does a machine sort the good from the bad? That’s exactly what Sentiment Analysis with Classical ML solves. It teaches a computer to label text as positive or negative,
Sentiment Analysis with Classical ML: Teach a Model to Read Emotion Read More »
Bag of Words counts your words. TF-IDF weighs them. But neither one understands them. Ask either method whether “cat” and “kitten” are related, and you get silence. Both words are just columns in a spreadsheet, as far apart as “cat” and “airplane.” That gap is exactly what word embeddings close. In this article, we walk
Word Embeddings Explained: How Machines Learn What Words Mean Read More »
Words feel simple to us. To a machine learning model, though, words mean nothing at all. A model only understands numbers. So before any NLP project can move forward, we need a reliable way to turn sentences into numbers. That’s exactly where Bag of Words and TF-IDF come in, and this article walks through both,
Bag of Words and TF-IDF: Turning Text Into Numbers a Machine Can Learn From Read More »
Machines do not read the way humans do. A sentence like “I loved the movies!” means nothing to a computer until someone breaks it down. That breakdown process has a name: text preprocessing in NLP. It sits at the very start of every natural language project, and it quietly does most of the heavy lifting.
Text Preprocessing in NLP: Tokenization, Stemming, and Lemmatization Read More »
Open any app on your phone. Check your email. Scroll through reviews before you buy something. In every case, you meet text data. It surrounds you all day, every day. Yet most beginners in data science skip past it. They jump straight to numbers and spreadsheets instead, because rows and columns feel safe and familiar.
Introduction to Text Data and NLP Basics Read More »
Forecasting theory is one thing. A working project is another thing entirely. That gap is exactly why this sales forecasting mini project exists. Instead of another isolated lesson on trend, seasonality, or holidays, this project connects every piece into one real pipeline. You move from a raw CSV file to a forecast a store manager
Sales Forecasting Mini Project: A Complete Prophet Pipeline on Real Retail Data Read More »
This article is the companion piece to Intelevo Episode 89. Watch the full video walkthrough on the Intelevo YouTube channel for the live demo, then use this article to review the ideas and code at your own pace. Forecasting sounds intimidating. However, it doesn’t have to be. In this guide, you’ll learn Facebook Prophet forecasting
Forecasting with Facebook Prophet: A Simple, Practical Guide Read More »