Machine Learning with K-Nearest Neighbors

Curriculum

• KNN Classifier – Theory Free

  3 Lessons 13 Min

  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.

  What does the course cover? Free Motivation Free Math Prerequisites: Distance Metrics Free
• Setting up the Environment Free

  2 Lessons 4 Min

  In advance of the hands-on part of the course, this section guides you through the installation process of relevant Python packages.

  Setting up the Environment Free Installing the Relevant Packages Free
• KNN Classifier – Practical Example

  8 Lessons 37 Min

  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’.

  Random Dataset: Generating the Dataset Free Random Dataset: Visualizing the Dataset Free Random Dataset: Classification Free Random Dataset: How to Break a Tie Random Dataset: Decision Regions Random Dataset: Choosing the Best K-value Random Dataset: Grid Search Random Dataset: Model Performance
• KNN Regressor

  3 Lessons 19 Min

  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.

  Theory with a Practical Example KNN vs Linear Regression: A Linear Problem KNN vs Linear Regression: A Non-linear Problem
• Pros and Cons of the KNN Algorithm

  1 Lesson 7 Min

  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.

  Pros and Cons