Day 9 SVM and clustering IN Machine Learning

Machine learning isn't just about prediction—it's about finding the hidden structure in chaotic data. Today, we conquer two essential techniques: Support Vector Machines (SVM) to draw the perfect decision boundaries, and K-Means Clustering to uncover groups we didn't even know existed.

What you’ll learn:

Support Vector Machines (SVM): Understanding the "Margin" (the street) between classes and how Kernels allow us to draw complex, non-linear boundaries.
K-Means Clustering: The logic behind unsupervised "center-finding" and how to determine the optimal number of groups (k) in your data.
Supervised vs. Unsupervised: Knowing when to use labeled data for classification and when to let the algorithm find its own patterns.

Challenge: Use K-Means on a dataset without labels. How many clusters (k) did you choose, and why? Did the model find groups that make sense to your human intuition?

#machinelearning #clustering #kmeans #datascience #unsupervisedlearning #python

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