Simple Nearest Neighbors Regression and Classification
Formulate small examples of KNN classification by hand
Implement a KNN Classification algorithm in Python
Implement a KNN Regression algorithm in Python
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.
Skills you will develop
Learn step-by-step
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Understanding the Basic Structure of a KNN model
Computing a simple KNN by hand
Looking at an example of a KNN in action in Python
Implementing an example KNN Regression in Python
Implementing an example KNN Classification in Python
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In a split-screen video, your instructor guides you step-by-step
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