Preface

When Marc Andreessen (https://www.crunchbase.com/person/marc-andreessen) wrote his famous article Why Software Is Eating The World in the Wall Street Journal, https://bit.ly/MarcAndreessen, he described a reality in which every company would be required to become a software company. The power of software was too great, its reach too vast. Companies could ignore it at their own peril. We are at the same inflection point now with Artificial Intelligence (AI).

There is a complexity to the field of AI that makes it both daunting for newcomers but also challenging for those already in it to ensure they have all the different areas covered. Aspects such as bias in models and data, interpretability/explainability, and even managing data science packages can be skills that aren't understood, even though they are critical in being able to build AI systems that will power our world. These concepts and more are no longer going to be optional. Too many resources leave this and many other areas of practical data science out.

After you are done reading this book, you'll wonder how anyone can be in this field and not have an understanding of core concepts such as proximity bias, using Anaconda Distribution, and how Shapley values tell you how features influence a model. All of this is knowledge that you will soon possess. We'll focus on the pragmatic and applicable as we use analogies to solidify your understanding. By the end, you'll be well positioned to take your knowledge of data science to the next level.