Machine Learning with K-Nearest Neighbors
K Nearest Neighbors - also known as KNN, is one of the most popular AI algorithms thanks to its simplicity of use and relatively high level of accuracy compared to more sophisticated algorithms. The KNN machine learning model has a very fast training process, making it a good machine learning algorithm to analyze multiclass datasets right off the bat. In these free Machine Learning with KNN pdf course notes, you will learn about the algorithm’s pros and cons, defining distance metrics, the important steps in creating a KNN model and the most commonly used performance metrics.
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Who is it for
Aspiring data scientists, business analysts, machine learning engineers, data engineers, and individuals who are looking to utilize the supervised machine learning capabilities of the KNN classifier in their data analyses will find great value in these Machine Learning with K Nearest Neighbors summary notes.
How it can help you
As part of the widely popular sklearn machine learning library, the KNN classifier is essential in executing predictive analytics and classification tasks. By studying our KNN course lecture notes you will add one more skill to your machine learning toolkit, opening more opportunities for career growth and exciting projects.
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