Introducing ML
In Chapter 1, Data Science Terminology, we defined ML as giving computers the ability to learn from data without being given explicit rules by a programmer. This definition still holds true. ML is concerned with the ability to ascertain certain patterns (signals) out of data, even if the data has inherent errors in it (noise).
ML models are able to learn from data without the explicit direction of a human. That is the main difference between ML models and classical non-ML algorithms.
Classical algorithms are told directly by a human how to find the best answer in a complex system, and the algorithm then achieves these best solutions, often working 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 order to tell the algorithm what to do. In ML, the model is not told the best solution and, instead, is given several examples of the problem and told to figure out the best solution...
