Logistic Regression: The Workhorse of Yes or No ProblemsUnderstand logistic regression from scratch as a weighted sum passes through the sigmoid function to produce probabilities for yes-or-no predictions.6 min read·tharunpandya.hashnode.dev1Sep 25
The Sigmoid Function: Squashing the World Into 0 and 1Learn how the sigmoid function turns raw model scores into values between 0 and 1, with intuition, math, probability, and Python examples.7 min read·tharunpandya.hashnode.dev1Sep 19
From Lines to Decisions: Why Regression Fails at ClassificationLearn why linear regression fails at classification, how predictions can go below 0 or above 1, and why sigmoid is needed for probabilities.9 min read·tharunpandya.hashnode.dev1Aug 31
Polynomial Regression: Teaching a Line to BendLearn polynomial regression from scratch using a delivery-time example, x^2, feature engineering, and gradient descent to fit curved patterns.10 min read·tharunpandya.hashnode.dev1Aug 22
Feature Scaling in Machine Learning: Why It MattersLearn why feature scaling matters in machine learning and how Min-Max, Mean Normalization, and Z-score scaling help gradient descent learn better.10 min read·tharunpandya.hashnode.dev1Aug 19
