Simple Nearest Neighbors Regression and Classification

提供方
Coursera Project Network
在此指導項目中,您將:

Formulate small examples of KNN classification by hand

Implement a KNN Classification algorithm in Python

Implement a KNN Regression algorithm in Python

Clock2 hours
Intermediate中級
Cloud無需下載
Video分屏視頻
Comment Dots英語(English)
Laptop僅限桌面

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.

您要培養的技能

Statistical AnalysisMachine LearningPython ProgrammingK-Nearest Neighbors Algorithm (K-NN)Classification Algorithms

分步進行學習

在與您的工作區一起在分屏中播放的視頻中,您的授課教師將指導您完成每個步驟:

  1. Understanding the Basic Structure of a KNN model

  2. Computing a simple KNN by hand

  3. Looking at an example of a KNN in action in Python

  4. Implementing an example KNN Regression in Python

  5. Implementing an example KNN Classification in Python

指導項目工作原理

您的工作空間就是瀏覽器中的雲桌面,無需下載

在分屏視頻中,您的授課教師會為您提供分步指導

常見問題

常見問題

還有其他問題嗎?請訪問 學生幫助中心