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K Nearest Neighbor (KNN) in 15 Minutes! | Machine Learning Basics | By Dr. Ry @Stemplicity
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Hello everyone and welcome to this tutorial on K-Nearest Neighbors.
In this tutorial, we will cover the theory and intuition behind the K-Nearest Neighbors classifiers
So what are K-nearest Neighbors classifiers?
K-nearest neighbor algorithm (KNN) is a classification algorithm that works by finding the most similar data points in the training data, and attempt to make an educated guess based on their classifications.
After completing this tutorial, students will be able to:
o Understand the theory behind K-nearest neighbor classifiers
o Calculate the Euclidean distance between 2 points in the training data-set
o Understand the impact of changing the K parameter on model accuracy
I hope you will enjoy this tutorial!
For more information on this, here’s a link to my new machine learning Classification course on Udemy:
Here’s a link to my new machine learning regression course on Udemy:
Subscribe to my channel to get the latest updates, we will be releasing new videos on weekly basis:
In this tutorial, we will cover the theory and intuition behind the K-Nearest Neighbors classifiers
So what are K-nearest Neighbors classifiers?
K-nearest neighbor algorithm (KNN) is a classification algorithm that works by finding the most similar data points in the training data, and attempt to make an educated guess based on their classifications.
After completing this tutorial, students will be able to:
o Understand the theory behind K-nearest neighbor classifiers
o Calculate the Euclidean distance between 2 points in the training data-set
o Understand the impact of changing the K parameter on model accuracy
I hope you will enjoy this tutorial!
For more information on this, here’s a link to my new machine learning Classification course on Udemy:
Here’s a link to my new machine learning regression course on Udemy:
Subscribe to my channel to get the latest updates, we will be releasing new videos on weekly basis:
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