Hello everyone, and welcome to Machine Learning Using C# and .NET. Our goal in this book is to expose you, a seasoned C# .NET developer, to the many open source machine learning frameworks that are available, as well as examples of using those packages. Along the way, we'll talk about logging, facial and motion detection, decision trees, image recognition, intuitive deep learning, quantum computing, and more. In many cases, you'll be up and running within minutes. It's my true hope that there is something for everyone in this series. Most importantly, having dealt with developers for 30 years now, here's why I wrote this book.
As a lifelong Microsoft developer, I have often watched developers struggle to find the resources needed for everyday problems. Let's face it, none of us have the time to do things the way we like, and few of us are fortunate enough to work in a true research and development unit. We've made quite a journey over the years though, from those of us old enough to remember having the sentinel copy of the C programmers' reference and 50 other books on our desk, to now being able to type in a quick search into Google and get exactly (okay, sometimes exactly) what we are looking for. But now that the age of AI is among us, things take a bit of a different turn. As C# developers, Google search isn't always our best friend when it comes to machine learning because almost everything being used is Python, R, MATLAB, and Octave. We also have to remember that machine learning has been around for many years; it's just recently that corporate America has embraced it and we're seeing more and more people become involved. The computing power is now available, and the academia has made incredible strides and progress in bringing it out into the world. But the world, my friends, as you have no doubt heard, is a scary place! Where is a C# .NET developer to turn? Let's start answering this question with a short story in the next section, which, unfortunately, is as true as the sky is blue. At least here in sunny Florida!
In this chapter, we are going to learn the following topics:
- Data mining
- Artificial Intelligence (AI) and bio-AI
- Deep learning
- Probability and statistics
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Whether to buy, build or open source