In this video, we’re breaking down two of the most commonly used algorithms in machine learning: K-Nearest Neighbors, also known as KNN, and Support Vector Machine, or SVM. If you’re looking to understand how these models work and when to use them, you’re in the right place.
We’ll start with KNN—a simple but powerful method that makes decisions by looking at its nearest neighbors in the data. Then, we’ll move on to SVM, which finds the best boundary between different groups using lines or curves. We’ll also compare both models side by side, talk about where they perform best, and walk through real-world examples using important metrics like accuracy, precision, and recall.
Whether you're just getting started or building on your machine learning knowledge, this video will help you understand these topics in a simple and practical way.
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