أبرز محتويات الدورة
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.
حول مقدم الدورة
Coursera provides access to more than 3000+ courses across a wide variety of subjects in parntership with different universities and organizations.