Now that we have whetted our appetite for machine learning, it is time to delve a little deeper into the different parts that make up a typical machine learning system.
Far too often, you hear someone throw around the phrase, just apply machine learning to your data!, as if that will instantly solve all your problems. You can imagine that the reality of this is much more intricate. Although, I will admit that nowadays it is incredibly easy to build your own machine learning system simply by cutting and pasting just a few lines of code from the internet. However, in order to build a system that is truly powerful and effective, it is essential to have a firm grasp of the underlying concepts and an intimate knowledge of the strengths and weaknesses of each method. So don't worry if you aren't considering yourself a machine learning...
