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 logistic regression from scratch

In this section, we will walk through the implementation of logistic regression in Python within the packtml package. We will start off with a brief recap of what logistic regression seeks to accomplish and then go over the source code and look at an example.



Recall that logistic regression seeks to classify a sample into a discrete category, also known as classification. The logistic transformation allows us to transform the log odds that we get from the inner product of our parameters and X.

Notice that we have three Python files open. One is, from within the utils directory inside of packtml; another is, from within the regression library in packtml; and the final one is an file, inside the examples directory and regression.

We will dive right into the code base using the following steps:

  1. We will start with the file. There are two functions that we will be using here...