Summary
Our exploration into the world of ML has revealed a vast landscape that extends well beyond the foundational techniques of linear and logistic regression. We delved into decision trees, which provide intuitive insights into data through their hierarchical structure. Naïve Bayes classification offered us a probabilistic perspective, showing how to make predictions under the assumption of feature independence. We ventured into dimensionality reduction, encountering techniques such as feature extraction, which help overcome the COD and reduce computational complexity.
k-means clustering introduced us to the realm of UL, where we learned to find hidden patterns and groupings in data without pre-labeled outcomes. Across these methods, we’ve seen how ML can tackle a plethora of complex problems, from predicting categorical outcomes to uncovering latent structures in data.
Through practical examples, we’ve compared and contrasted SL, which relies on labeled...
