Machine learning is basically teaching computers to solve big problems based on either example data or past experiences. Example data, is purely unlabeled, with unknown and undetected structure. Your power would rely on you guessing the hidden structure which ultimately leads in you learning more about it. Using technical terminologies, unsupervised learning best describes the latter. Past experiences on the other hand, is real data with clear labels and answers to the question you are trying to answer.
For those considering an autodidactic alternative, this is for you. You can't go deeply into every machine learning topic. There's too much to learn, and the field is advancing rapidly. Motivation is far more important than micro-optimizing a learning strategy for some long-term academic or career goal. If you're trying to force yourself forward, you'll slow down.