It wouldn't make sense to continue without a concrete definition of what machine learning is. Well, let's back up for a minute. In Chapter 1, How to Sound Like a Data Scientist, we defined machine learning as giving computers the ability to learn from data without being given explicit rules by a programmer. This definition still holds true. Machine learning is concerned with the ability to ascertain certain patterns (signals) out of data, even if the data has inherent errors in it (noise).
Machine learning models are able to learn from data without the explicit help of a human. That is the main difference between machine learning models and classical algorithms. Classical algorithms are told how to find the best answer in a complex system and the algorithm then searches for these best solutions and often works faster and more efficiently than a human. However, the bottleneck here is that the human has to first come up with the best solution. In machine learning,...