Introducing you to the exciting topic of machine learning with the K-nearest neighbors algorithm using Python’s scikit-learn library.
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.
Aiming to upgrade your machine learning skills? This course will help you:
In this introductory section, we motivate the usage of a K-Nearest Neighbor classifier and give 2 intuitive examples. Following that is a short math refresher where we talk about the various ways in which the distance between 2 points in space can be defined. This would later serve us well when building our KNN model in Python.
In advance of the hands-on part of the course, this section guides you through the installation process of relevant Python packages.
To apply your skills in practice, you will first learn how to generate a random set of points, distribute them into 3 classes, and place them on the coordinate system. We will then use this dataset to train and test a KNN classification algorithm with the help of Python’s scikit-learn library. We will look at some edge cases that can arise during the classification process and discover how to handle them. Next, we will guide you through the process of building the so-called decision regions, which are a great way of visualizing the performance of your model. Finally, we will find out how to choose the best model parameters using a technique called ‘grid search’.
Continuing the practical part of the course, we will dive into solving regression tasks using the K-Nearest Neighbors method. Similar to what we did in the previous section, we will tackle this problem by generating 2 random datasets. One would represent a linear problem, while the other would be non-linear. We will apply a linear (parametric) model and a KNN (non-parametric model) on both datasets and argue which one performs better.
In this final section of the course, the pros and cons of the KNN algorithm are discussed at length. We will study this method’s limitations, together with its strong sides.
Student feedback
“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.”
Content Creator at 365 Data Science
Machine Learning with K-Nearest Neighbors
with Hristina Hristova
