DKDivyajot Kaurinai-beginners-journey.hashnode.dev·3d ago · 6 min readPart 14: Understanding Decision Trees in Machine LearningImagine you're planning a weekend outing with your friends. You start asking a series of simple questions: Is the weather sunny? Yes → Go for a picnic. No → Ask another question. Is it raining h00
DKDivyajot Kaurinai-beginners-journey.hashnode.dev·Jul 3 · 5 min readPart 13: K-Nearest Neighbors (KNN): Making Predictions Using Similar DataImagine you're visiting a new city and want to try a good restaurant. You don't know which restaurant is the best, so what do you do? You ask a few people nearby for recommendations. If most people r00
DKDivyajot Kaurinai-beginners-journey.hashnode.dev·Jun 26 · 6 min readPart 12: Logistic Regression: How Machines Learn to Make Yes-or-No DecisionsImagine you're ordering food online. Before you place your order, the app might show you a message like: "Will your order arrive within 30 minutes?" The app doesn't know the future, but based on facto00
DKDivyajot Kaurinai-beginners-journey.hashnode.dev·Jun 20 · 4 min readPart 11: What is Gradient Descent?In the previous blog, we learned how Linear Regression finds a best-fit line to make predictions. But an important question remains: How does the model know which line is the best? The answer lies in 00
DKDivyajot Kaurinai-beginners-journey.hashnode.dev·Jun 11 · 4 min readPart 10: Introduction to Linear RegressionSo far in this series, we've explored datasets, preprocessing, train-test splitting, overfitting, underfitting, bias, and variance. Now it's time to dive into the algorithms that actually make predict00
