Think of going into a library with only a single book there, and you are expected to arrange the books. You would most likely begin sorting books by appearance or feel, similar cover art or length or topic, although nobody informed you of the genres. It is the key idea of unsupervised learning: it seeks structure of data in case of no annotated responses. This tutorial explains what is meant by unsupervised learning and why this type of learning is alternative to supervised learning and provides a summary of the most common types of algorithms: Clustering and dimensionality reduction . It also introduces the mathematical intuition behind the underlying basic mathematics of such applications and provides line by line explanation of the K-Means and PCA algorithms in Python. What is Unsupervised Learning — and why it matters? Unsupervised learning Unsupervised learning refers to a group of methods used to determine patterns, clusters or order in data without any target labels. This i...
What is Supervised Machine Learning? Supervised Machine Learning is training a computer in the same way that we train children using examples. Let’s break it step by step Example with Fruits Consider a scenario where you are training a child on how to identify fruits. You show an apple and say, “This is an apple.” You show a banana and say, “This is a banana.” After watching lots of apples and bananas the child begins to notice the patterns : Apples are usually round, red, or green. Bananas are long and yellow. Next time even when you give the child a new apple (not the one you showed him or her before) the child is able to say: “That looks like an apple!” How Computers Learn in the Same Way In supervised ML: Input (Data) = The items that we present to the computer (such as images of fruits, emails or medical records of patients). Output (Label/Correct Answer) = What the data really is (Apple, Banana, Spam, Not Spam, Healthy, Sick). Lea...