In short, since your main task is to select a learning algorithm and train it on some data, the two things that can go wrong are “bad algorithm” and “bad data.” Let’s start with examples of bad data. Insufficient Quantity of Training Data For a toddler to learn what an apple is, all it takes is for you to point to an apple and say “apple” (possibly repeating this procedure a few times). Now the child is able to recognize apples in all sorts of colors and shapes. Genius. Machine Learning is not quite there yet; it takes a lot of data for most Machine Learn‐ ing algorithms to work properly. Even for very simple problems you typically need thousands of examples, and for complex problems such as image or speech recogni‐ tion you may need millions of examples (unless you can reuse parts of an existing model). Nonrepresentative Training Data In order to generalize well, it is crucial that your training data be representative of the new cases you want to generalize to. This is true wheth...