Machine Learning with K-Nearest Neighbors

Introducing you to the exciting topic of machine learning with the K-nearest neighbors algorithm using Python’s scikit-learn library.

with Hristina Hristova

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Course Overview

Practical knowledge of various machine learning algorithms is essential for machine learning enthusiasts and experts alike. In this course, we focus extensively on one of the most intuitive and easy-to-implement ML algorithms out there – K-nearest neighbors, or KNN for short. Step by step, we will first lay the foundations and expand your mathematical toolbox. Then, you will progress to coding and using Python’s scikit-learn library to solve a randomly generated classification problem. Finally, you will apply KNN to a couple of regression tasks. In other words – you will learn all the subtleties that should be considered when applying the KNN algorithm in your future practice.

17 High Quality Lessons
3 Practical Tasks
1 Hours of Video
Certificate of Achievement

Skills you will gain

machine learningmathematicsprogrammingpythontheory

What You'll Learn

Aiming to upgrade your machine learning skills? This course will help you:

Get acquainted with different distance metrics
Grasp the working of the KNN algorithm
Generate random datasets
Use scikit-learn’s KNN algorithms
Construct decision boundaries
Understand the pros and cons of the KNN algorithm

“KNN is an essential step in the development of every machine learning practitioner. It serves as a perfect example of a rather simple algorithm performing incredibly well and allowing for diverse practical applications. Moreover, it is great fun to visually compare the results from different KNN models on the same dataset.”

Hristina Hristova
Content Creator at 365 Data Science
Machine Learning with K-Nearest Neighbors

with Hristina Hristova

Start Course