Skip to main content

Feedback Form

 

Comments

Popular posts from this blog

Model Evaluation: Measuring the True Intelligence of Machines

  Model Evaluation: Measuring the True Intelligence of Machines Imagine you’re a teacher evaluating your students after a semester of classes. You wouldn’t just grade them based on one test—you’d look at different exams, assignments, and perhaps even group projects to understand how well they’ve really learned. In the same way, when we train a model, we must evaluate it from multiple angles to ensure it’s not just memorizing but truly learning to generalize. This process is known as Model Evaluation . Why Do We Need Model Evaluation? Training a model is like teaching a student. But what if the student just memorizes answers (overfitting) instead of understanding concepts? Evaluation helps us check whether the model is genuinely “intelligent” or just bluffing. Without proper evaluation, you might deploy a model that looks good in training but fails miserably in the real world. Common Evaluation Metrics 1. Accuracy Analogy : Like scoring the number of correct answers in ...

What is Unsupervised Learning?

  🧠 What is Unsupervised Learning? How Machines Discover Hidden Patterns Without Supervision After exploring Supervised Learning , where machines learn from labeled examples, let’s now uncover a more autonomous and mysterious side of machine learning — Unsupervised Learning . Unlike its "supervised" sibling, unsupervised learning doesn’t rely on labeled data . Instead, it lets machines explore the data, find patterns, and groupings all on their own . 🔍 Definition: Unsupervised Learning is a type of machine learning where the model finds hidden patterns or structures in data without using labeled outputs. In simpler terms, the machine is given data and asked to "make sense of it" without knowing what the correct answers are . 🎒 Analogy: Like a Tourist in a Foreign Country Imagine you arrive in a country where you don’t speak the language. You walk into a market and see fruits you've never seen before. You start grouping them by size, color, or ...