K-NEAREST NEIGHBORS & SVM in Machine Learning
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K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) are two of the most widely used algorithms in Machine Learning. In this video, we dive deep into how these models work, how they differ, and when to use each one for the best performance based on your dataset and problem type.
📊 Whether you're a beginner or an advanced learner, this session simplifies complex ML concepts into clear, practical insights that you can apply right away!
💡 What is K-Nearest Neighbors (KNN)? – A simple yet powerful lazy learning algorithm for classification and regression.
📊 What is Support Vector Machine (SVM)? – Learn about hyperplanes, margins, and how SVM handles linear and non-linear separations.
📈 KNN vs SVM – Discover which model performs best for different datasets and why.
🧠 Real-World Use Cases & Metrics – Explore applications and evaluation using Accuracy, Precision, Recall, and more.
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