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KNN and Naive Bayes Algorithm

Beginner level 1.5 learning hrs 520 Learners

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About this course

KNN and Naive Bayes are widely used Machine Learning algorithms. KNN is a simple, non-parametric, and lazy classification algorithm to use a dataset where the data points are categorized into different classes to predict a new sample point classification. A Naïve Bayes classifier is an algorithm that uses Bayes' theorem to classify objects.In this course,we will discuss the pros and cons of the KNN, Naive Bayes theorem and understand how to decide the optimal value for K and train the model with the various variables in the dataset to predict the species. Also discuss Naive Bayes model to predict the species with respect to the same variables in the dataset.

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Course outline

Introduction to K-Nearest Neighbor

K-Nearest Neighbor in R : Hands-on

Introduction Naive Bayes Algorithm

Naive Bayes in R : Hands-on

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KNN and Naive Bayes Algorithm

1.5 Hours

Beginner

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520 learners enrolled so far

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