This book is about approaching tough problems. Machine learning is an amazing application of computation because it tackles problems that are straight out of science fiction. These algorithms can solve voice recognition, mapping, recommendations, and disease detection. The applications are endless, which is what makes machine learning so fascinating.
This flexibility is also what makes machine learning daunting. It can solve many problems, but how do we know whether we’re solving the right problem, or actually solving it in the first place? On top of that sadly much of academic coding standards are lax.
Up until this moment there hasn’t been a lot of talk about writing good quality code when it comes to machine learning and that is unfortunate. The ability for us to dis‐ seminate an idea across an entire industry is based on our ability to communicate it effectively. And if we write bad code, it’s doubtful a lot of people will listen. Writing this book is my answer to that problem. Teaching machine learning to people in an easier to approach way. This subject is tough, and it’s compounded by hard to read code, or ancient C implementations that make zero sense.
While a lot of people will be confused as to why this book is written in Ruby instead of Python, it’s because writing tests in Ruby is a beautiful way of explaining your code. The entire book taking this test driven approach is about communication, and com‐ municating the beautiful world of Machine Learning.
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