دانلود کتاب هنر یادگیری ماشین

Machine learning! With such a science fiction-ish name, one might expect it to be technology that is strictly reserved for highly erudite specialists. Not true. Actually, machine learning (ML) can easily be explained in commonsense terms, and anyone with a good grasp of charts, graphs, and the slope of a line should be able to both understand and productively use ML. Of course, as the saying goes, “The devil is in the details,” and one must work one’s way through those details. But ML is not rocket science, in spite of it being such a powerful tool. ML is all about prediction. Does a patient have a certain disease? Will a customer switch from her current cell phone service to another? What is actually being said in this rather garbled audio recording? Is that bright spot observed by a satellite a forest fire or just a reflection? We predict an outcome from one or more features. In the disease diagnosis example, the outcome is having the disease or not, and the features may be blood tests, family history, and so on. All ML methods involve a simple idea: similarity. In the cell phone service example, how do we predict the outcome for a certain customer? We look at past customers and select the ones who are most similar in features (size of bill, lateness record, yearly income, and so on) to our current customer. If most of those similar customers bolted, we predict the same for the current one. Of course, we are not guaranteed that outcome, but it is our best guess.