Mostafa Samir Abd El-Fattah - How Machine Learning Works MEAP V05

Thank you for purchasing the MEAP of How Machine Learning Works . I hope this book would serve as a step forward in your career and an aid in the journey of making your products smarter.

Machine learning is one of the hottest topics out there. From autonomous cars to intelligent personal assistants and smart business analysis and decision making, you can find machine learning almost anywhere. With that abundance, it makes perfect sense that there is a lot of resources out there teaching machine learning and making it easier by the day for anyone to get up and running with a functional machine learning product. However, machine learning is quite different from other kinds of programming; it's an intersection of multiple fields that include programming, mathematics, statistics and computer science. Unfortunately, for an average software engineer like me, when I started learning ML myself I had a lot of difficulty finding a resource that presented ML form all these different aspects, showing how all these fields work together coherently and in a principled manner to give all these ML tools and methods. What I was able to find is either how to use the off-the-shelf libraries with neat programmatic recipes that hides all the meat of the algorithms, or a harsh academia with the mathematical foundations being seemingly distant from what one would use on their day-to-day work. That challenging link from understanding the mathematical theory to the internals of how the various ML algorithms work and how they are implemented; that link seemed missing to me. This book takes on the challenge and attempts to provide that missing link; an introduction to machine learning in which both practice and theory collaborate into giving you a deeper and working understanding of the field.

No fancy mathematical knowledge is required from you; only your basic algebra. The book teaches the math it needs along the way, and it doesn't teach how to churn numbers like a regular math textbook. Not that learning to churn numbers is not important, but the book is more concerned with introducing the meaning and intuition behind the math in order to understand how they serve as the foundations of machine learning algorithms. And as the book is mainly written for python software engineers and developers with little to no knowledge of machine learning, the book takes a practice-first approach; we start by encountering a real-world problem and see how to create a product that solves it using machine learning software, and from our practice we start poking under the hood and discovering the basis of the algorithms and how and why they work; thus completing the circle of a deep and practical understanding of machine learning.

As we said before, the book takes on a challenging task, and it takes some time to write the chapters in an adequate manner that is worthy of your reading time. So we thank you for your patience and support during the time the book is developed. Your feedback will be invaluable in improving the book as we go, so please do not hesitate to share your questions, thoughts, comments and suggestions in the liveBook Discussion Forum.

Thanks again for your interest in How Machine Learning Works . We hope you have a fruitful reading!

Mostafa Samir

Machine learning is one of the hottest topics out there. From autonomous cars to intelligent personal assistants,smart business analysis, and decision making, you can find machine learning almost anywhere. With that abundance, it makes perfect sense that there is a lot of resources out there teaching machine learning and making it easier by the day for anyone to get up and running with a functional machine learning product. However, machine learning is quite different from other kinds of programming; it's an intersection of multiple fields that include programming, mathematics, statistics and computer science. Unfortunately, for an average software engineer like me, when I started learning ML by myself I had a lot of difficulty finding a resource that presented ML form all these different aspects, showing how all these fields work together coherently and in a principled manner to give us all these ML magic. What I was able to find is either how to use the off-the-shelf libraries with neat programmatic recipes that hides all the meat of the algorithms, or a harsh academia with the mathematical foundations being seemingly distant from what one would use on their day-to-day work. That challenging link from understanding the mathematical theory to the internals of how the various ML algorithms work and how they are implemented; that link seemed missing to me. This book takes on the challenge and attempts to provide that missing link; an introduction to machine learning in which both practice and theory collaborate into giving you a deeper and working understanding of the field.

Why this book

Well, we said earlier that we're writing this book to try and provide a picture of machine learning where we can use the programmatic tools while understanding their foundations and how they work on the inside, but a question remains: why?! Why is understanding the internals of machine learning so important? Why do we need to write a book about it? Why can't we simply use libraries where we specify a model, train it and use its predictions without worrying about what the library is doing under the hood? The answer to that question is that these tools and libraries are leaky abstractions . Leaky abstractions are abstractions that leak aspects of its hidden details, usually when something goes wrong with them.

Think about the brakes system in a car; to slow down a car or bring it to stop, all you have to do on your end is simply step on the pedals. Under the hood, that pedal is abstracting a complex network of pistons, pipes, hoses, hydraulic fluids and discs that all work together to bring your car to stop. The pedal is shielding you from all these intricate inner working by simply requiring you to step on it. Unfortunately, this is not the case when something goes wrong with underlying mechanism; if a pipe got pinched or the hydraulic fluid leaked out, then the system is going to stop working and the pedal can't do anything for you at this moment. The brakes pedal is an example of a leaky abstraction.

In a 2002 article , Joel Spolsky, the co-founder of Stack Overflow and Trello, coined the law of leaky abstractions , which states that:

All non-trivial abstractions, to some degree, are leaky

This law states that the more complexity an abstraction is hiding, the more probable that it is leaky. In software development, abstractions are inevitable: if we want to efficiently manage the ever growing complexity of a software system, then there is no escape from using abstractions. In the same time, we still don't want to drown in the leakage of our abstractions (pardon the pun); hence, we need to have some understanding about how that abstraction is working under the hood. Think back to the car brakes system: if you are the one who is making the car or maintaining it, you can't afford not knowing how a brakes system works and just treat it as a black-box. If the slightest mistake happened during the installation or operation, you're probably going to be in trouble.

Machine learning libraries are no exception from that law. If you use a library to write something like