Smith - Supervised machine learning with Python: develop rich Python coding practices while exploring supervised machine learning

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Taylor Smith is a machine learning enthusiast with over five years of experience who loves to apply interesting computational solutions to challenging business problems. Currently working as a principal data scientist, Taylor is also an active open source contributor and staunch Pythonista.

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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.

This book is for aspiring machine learning developers who want to get started with supervised learning. Intermediate knowledge of Python programming and some fundamental knowledge of supervised learning is expected.

, First Step toward Supervised Learning , covers the basics of supervised machine learning to get you prepared to start tackling problems on your own. The chapter comprises four important sections. First, we will get our Anaconda environment set up and make sure that we are able to run the examples. Over the next couple of sections following that, we will cover a bit more of the theory behind machine learning, before we start implementing algorithms in the final section, where we'll get our Anaconda environment set up.

, Implementing Parametric Models , dives into the guts of several popular supervised learning algorithms within the parametric modeling family. We'll start this section by formally introducing parametric models, then we'll focus on two very popular parametric models in particular: linear and logistic regression. We'll spend some time understanding the inner workings and then jump into Python and actually code them from scratch.

, Working with Non-Parametric Models , explores the non-parametric model family. We will start by covering the bias-variance trade-off, and explain how parametric and non-parametric models differ at a fundamental level. We will then get into decision trees and clustering methods. Finally, we'll address some of the pros and cons of non-parametric models.

, Advanced Topics in Supervised ML , splits its time between two topics: recommender systems and neural networks. We'll start with collaborative filtering and then talk about integrating content-based similarities into your collaborative filtering systems. Finally, we'll get into neural networks and transfer learning.