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Series
A series on Supervised Learning covering core concepts, key algorithms, and real-world applications—explained simply with examples and projects to connect theory with practice.
Machine Learning can be broadly divided into different types, and as we covered earlier in our article (type of machine learning & basic workflow), one of the most widely used types is Supervised Learning. It’s the foundation behind many AI systems w...
In the early part of our Supervised Learning series, we introduced what supervised learning is and how it works. Now, in this second article, we’ll explore Logistic Regression — one of the most common and powerful classification algorithms. The goal ...
In our supervised learning series, we’ve explored the basics of classification algorithms and covered the concept of logistic regression. Now, let’s move to another popular algorithm: Decision Trees. Decision Trees are widely used in machine learning...
In our previous article, we explored how a Decision Tree works step-by-step and how it makes predictions in a simple and interpretable way. Now, let’s move to the next algorithm in our Supervised Learning series — one that takes decision trees to the...
When learning machine learning, some algorithms feel like “magic.” But K-Nearest Neighbors (KNN) is different; it’s one of the most intuitive algorithms to understand.It doesn’t build a complex model; instead, it relies on proximity and similarity, a...
Machine Learning algorithms come in all forms where some rely on distances, some on trees, and some on probabilities. Naive Bayes Classifier belongs to the last group as it predicts categories based on probability and prior knowledge. Let’s understan...
