Machine Learning Overview (page 4 of 13)

Machine learning may be categorized into supervised and unsupervised machine learning. Supervised machine learning is analogous to training a child how to ride a bike. We know the objective (ride a bike without falling) before we start the training process. We will hold the child's hand and use training wheels until the child learns how to ride the bike on their own without the training wheels. We know the target (objective) before we start. Contrarily, unsupervised machine learning does not specify a known target to the machine learning algorithm. In essence, we are asking the algorithm "what can you tell me about these data?" when performing an unsupervised problem. Clustering is an unsupervised example because we do not know the target clusters before starting the process.