Book Image

Supervised Machine Learning with Python

By : Taylor Smith
Book Image

Supervised Machine Learning with Python

By: Taylor Smith

Overview of this book

Supervised machine learning is used in a wide range of sectors, such as finance, online advertising, and analytics, to train systems to make pricing predictions, campaign adjustments, customer recommendations, and much more by learning from the data that is used to train it and making decisions on its own. This makes it crucial to know how a machine 'learns' under the hood. This book will guide you through the implementation and nuances of many popular supervised machine learning algorithms, and help you understand how they work. You’ll embark on this journey with a quick overview of supervised learning and see how it differs from unsupervised learning. You’ll then explore parametric models, such as linear and logistic regression, non-parametric methods, such as decision trees, and a variety of clustering techniques that facilitate decision-making and predictions. As you advance, you'll work hands-on with recommender systems, which are widely used by online companies to increase user interaction and enrich shopping potential. Finally, you’ll wrap up with a brief foray into neural networks and transfer learning. By the end of this book, you’ll be equipped with hands-on techniques and will have gained the practical know-how you need to quickly and effectively apply algorithms to solve new problems.
Table of Contents (11 chapters)
Title Page
Copyright and Credits
About Packt

Implementing KNNs from scratch

In this section, we will jump into the packtml code base, and see how we can implement it from scratch. We'll start by revisiting the classic algorithm we covered in the last section, and then we'll look at the actual Python code, which has some implementation changes.

Recall the archetypal KNN algorithm. The efficient implementation is going to be to pre-compute the distances and store them in a special heap. Of course, with most things in computer science, there's the clever way and then there's the easy-to-read way. We're going to do things a bit differently in an effort to maximize the readability, but it's the same fundamental algorithm.

KNN clustering

We've got two files we want to look at. The first is the source code in the packtml Python package. Second, we're going to look an example of the KNN applied to the iris dataset. Let's go ahead and jump over to PyCharm, where there are two files open. Inside of the clustering submodule, we have the file...