IT Professional Curricula     Enterprise Database Systems Solution Area     Math     The Math Behind Distance-based Models

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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.

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Objectives

Distance-based Models: Overview of Distance-based Metrics & Algorithms discover the key concepts covered in this course recall how distance-based models work at a high level and identify the use cases of such models describe the Hamming and Cosine distance metrics recount how the KNN and K-means algorithms use distance metrics to perform ML operations define and visualize two points in a two-dimensional space using Python calculate the Euclidean and Manhattan distance between two points using SciPy as well as your own function implement a Minkowski and Hamming distance calculator and use the built-in ones available in SciPy compute the cosine distance between vectors summarize the key concepts covered in this course