Machine Learning Pocket Reference

Preface

Machine learning and data science are very popular right now and are fast-moving targets. I have worked with Python and data for most of my career and wanted to have a physical book that could provide a reference for the common methods that I have been using in industry and teaching during workshops to solve structured machine learning problems.

This book is what I believe is the best collection of resources and examples for attacking a predictive modeling task if you have structured data. There are many libraries that perform a portion of the tasks required and I have tried to incorporate those that I have found useful as I have applied these techniques in consulting or industry work.

Many may lament the lack of deep learning techniques. Those could be a book by themselves. I also prefer simpler techniques and others in industry seem to agree. Deep learning for unstructured data (video, audio, images), and powerful tools like XGBoost for structured data.

I hope this book serves as a useful reference for you to solve pressing problems.

What to Expect

This book gives in-depth examples of solving common structured data problems. It walks through various libraries and models, their trade-offs, how to tune them, and how to interpret them.

The code snippets are meant to be sized such that you can use and adapt them in your own projects.

Who This Book Is For

If you are just learning machine learning, or have worked with it for years, this book should serve as a valuable reference. ...