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- Level Intermediate
- Duration 4 hours
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In this 2-hour long project-based course, we will explore the basic principles behind the K-Nearest Neighbors algorithm, as well as learn how to implement KNN for decision making in Python. A simple, easy-to-implement supervised machine learning algorithm that can be used to solve both classification and regression problems is the k-nearest neighbors (KNN) algorithm. The fundamental principle is that you enter a known data set, add an unknown data point, and the algorithm will tell you which class corresponds to that unknown data point. The unknown is characterized by a straightforward neighborly vote, where the "winner" class is the class of near neighbors. It is most commonly used for predictive decision-making. For instance,: Is a consumer going to default on a loan or not? Will the company make a profit? Should we extend into a certain sector of the market? Note: This course works best for learners who are based in the North America region. We're currently working on providing the same experience in other regions.Auto Summary
Discover the principles of the K-Nearest Neighbors algorithm with this engaging 2-hour project-based course on Coursera. Taught at an intermediate level, you'll learn to implement KNN for decision-making in Python. Perfect for those interested in personal development, the course is free and ideal for learners looking to enhance their data science skills.