There are many ways to get started with studying machine learning. I have previously written a lot about how to design your own curriculum and roadmap as an alternative to taking courses. This approach allows you to pick and choose free, or low-cost, resources from across the internet that suit both your learning style and budget. However, when you are just starting out on the beginning of your journey into machine learning it can often be useful to follow at least a short course that will guide you through the basic concepts first. This will give you a good foundational overview of the field and it will make it easier to design your own learning path and then continue on with deeper self-directed learning.

Academic programs are one way for professionals to stay current with today's most in-demand skills. With the skills shortage increasing and competition for talent raging through industry and among start-ups, training has become a priority. Many aspiring data professionals are sharpening their skills through online courses or attending industry conferences. However, sometimes you just want to go back to school, at least for a visit. Try the University of Washington or the University of California, Irvine.

This book, together with specially prepared online material freely accessible to our readers, provides a complete introduction to Machine Learning, the technology that enables computational systems to adaptively improve their performance with experience accumulated from the observed data. Such techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know.