I often get asked on how to get started with Machine Learning. Most of the time, people have troubles understanding the maths behind all things. And I have to admit, I don't like the maths either. Math is an abstract way of describing things. And I think the way Machine Learning is described is too abstract to understand it easily.
So in this article series I probably try to describe things with foo code or a bit of JS to explain what I'm talking about.
The first thing you have to know is that there are different concepts that allow different solutions.
I will dig into the more complex solutions later when you're ready for them. The problems are mostly divided in time-sensitive data (RNNs and RNN LSTMs), visual or pixel-related data (CNNs) and simple vectorized data (BNNs, BNs, BNNs and QNNs).
However, there are more complex architectures around from the engineering and biological perspective; and I think they're a quite powerful toolset to know - as they can be freely combined with "low-level" machine learning solutions.
Explanation of all those concepts would be too much right now, as most of them have other - more coincidental - use cases in other areas like NLP, parsing complex texts or describing artistic paintings.
I'm also trying not to be language specific and neither framework specific here. The demo codes are written in simple ES2017+ JavaScript, so you can easily try them out in node.js and the Web Browser (Chrome recommended) and fiddle with the demos to your liking.
The first topic I'll cover in this article is the raw basics of Neural Networks, Genetic Programming and Evolution.