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

Chapter 1. First Step Towards Supervised Learning

In this book, we will learn about the implementation of many of the common machine learning algorithms you interact with in your daily life. There will be plenty of math, theory, and tangible code examples to satisfy even the biggest machine learning junkie and, hopefully, you'll pick up some useful Python tricks and practices along the way. We are going to start off with a very brief introduction to supervised learning, sharing a real-life machine learning demo; getting our Anaconda environment setup done; learning how to measure the slope of a curve, Nd-curve, and multiple functions; and finally, we'll discuss how we know whether or not a model is good. In this chapter, we will cover the following topics:

  • An example of supervised learning in action
  • Setting up the environment
  • Supervised learning
  • Hill climbing and loss functions
  • Model evaluation and data splitting