Supervised machine learning is used in a wide range of sectors such as finance, online advertising, and analytics because it allows you to train your system to make pricing predictions, campaign adjustments, customer recommendations, and much more, giving the system the ability to self-adjust and make decisions on its own. The benefits this can give make 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. You'll embark on this journey with a quick overview and see how supervised machine learning differs from unsupervised learning. After that, we will explore parametric models such as linear and logistic regression, non-parametric methods such as decision trees, and various clustering techniques to facilitate decision-making and predictions. As we proceed, you'll work with recommender systems, which are widely used by online companies to increase user interaction and boost potential sales. Finally, we'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 to gain the practical know-how needed to quickly and powerfully apply supervised learning algorithms to new problems.
