Chevron Left
Simple Nearest Neighbors Regression and Classification に戻る

Coursera Project Network による Simple Nearest Neighbors Regression and Classification の受講者のレビューおよびフィードバック


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....

Simple Nearest Neighbors Regression and Classification : 1 - 1 / 1 レビュー

by Jianfeng G


T​his is probably the worst guided project that I have ever taken. The instruction was disorganized and moved too fast. The instuctor did not use a Jupyter notebook and simply copied and pasted codes, while expecting the learners to follow through!