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

Using transfer learning

In this section, we're going to take it one step further and explore the question of whether a neural network could learn from other neural networks and what they've already learned. We'll start by covering the concept of transfer learning, and then we'll get into some Python code.


Transfer learning is essentially the Frankenstein's monster of machine learning. The idea arose from this question: how can I take what some other network has already learned and go from there? We're basically going to do a brain splice between several different networks. This can be extremely valuable in cases where a network is trained on data that you don't have access to or the training process is the one that would have taken hours or days, as is commonly the case in text or image processing domains.

We don't want to retrain our model because it would take forever, but we want to take what we've already learned about the other two classes and start learning something else about the...