Machine learning: As discussed in previous article machine learning is the study and construction of algorithms that can learn from and make predictions on data. Such algorithms operate by building a model from sample inputs in order to make data-driven predictions or decisions expressed as outputs, rather than following strictly static instructions.
Machine learning can be divided into two types:
Supervised and unsupervised Learning
Supervised learning: It is the machine learning task of inferring a function from labeled training data. The training data consist of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal). A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples.
The computer is presented with example inputs and their desired outputs, given by a “teacher”, and the goal is to learn a general rule that maps inputs to outputs. Specifically, a supervised learning algorithm takes a known set of input data and known responses to the data (output), and trains a model to generate reasonable predictions for the response to new data.
Supervised learning problems are categorized into “regression” and “classification”problems. In a regression problem, we are trying to predict results within a continuous output, meaning that we are trying to map input variables to some continuous function. In a classification problem, we are instead trying to predict results in a discrete output. In other words, we are trying to map input variables into discrete categories.
Given data about the size of houses on the real estate market, try to predict their price. Price as a function of size is a continuous output, so this is a regression problem.
We could turn this example into a classification problem by instead making our output about whether the house “sells for more or less than the asking price.” Here we are classifying the houses based on price into two discrete categories.Classification of emails into spam and not spam is another example of the supervised learning.
Unsupervised learning, on the other hand, allows us to approach problems with little or no idea what our results should look like. We can derive structure from data where we don’t necessarily know the effect of the variables.
We can derive this structure by clustering the data based on relationships among the variables in the data.With unsupervised learning there is no feedback based on the prediction results, i.e., there is no teacher to correct you. It’s not just about clustering. For example, associative memory is unsupervised learning.
It is learning without a master. One basic thing that you might want to do with data is to visualize it. It is the machine learning task of inferring a function to describe hidden structure from unlabeled data. Since the examples given to the learner are unlabeled, there is no error or reward signal to evaluate a potential solution. This distinguishes unsupervised learning from supervised learning. Unsupervised learning uses procedures that attempt to find natural partitions of patterns.
With unsupervised learning there is no feedback based on the prediction results, i.e., there is no teacher to correct you. Under the Unsupervised learning methods no labeled examples are provided and there is no notion of the output during the learning process. As a result, it is up to the learning scheme/model to find patterns or discover the groups of the input data
Example Unsupervised learning:
Bunch of photos of random people but without information who is on which one and want to divide this data-set into 6 piles, each with photos of one individual.
Discovery of drugs based on the different types of molecules present in them.
Check out What is Machine Learning?