Distance-based Models: Overview of Distance-based Metrics & Algorithms

“Machine learning (ML) is widely used across all industries meaning engineers need to be confident in using it. Pre-built libraries are avAIlable to start using ML with little knowledge. However to get the most out of ML it s worth taking the time to learn the math behind it.

Use this course to learn how distances are measured in ML. Investigate the types of ML problems distance-based models can solve. Examine different distance measures such as Euclidean Manhattan and Cosine. Learn how the distance-based ML algorithms K Nearest Neighbors (KNN) and K-means work. Lastly use Python libraries and various metrics to compute the distance between a pAIr of points.

Upon completion you ll have a solid foundational knowledge of the mechanisms behind distance-based machine learning algorithms.”