Field of study that gives computers the ability to learn without being explicitly programmed. ~ Arthur Samuel (1959)
Each time you do a Google web search, the results were ranked and showed up using Machine Learning. When Facebook recognizes your friends in your pictures that is also Machine Learning, every time you are using your email and spam filters, that feature helps you to avoid tons of spam, that is again because using Machine Learning helps you to distinguish spam from non spam email messages.
This is exciting because it is fundamental to artificial intelligence (AI), many scientist believe that generating progress in this is through learning algorithms called neural networks, which mimic how the human brain works.
There are two principal basic problems in Machine Learning called:
- Supervised Learning
- Unsupervised Learning
Supervised learning is a type of machine learning algorithm that uses a known dataset (called the training dataset) to make predictions. The training dataset includes input data and response values. From it, the supervised learning algorithm seeks to build a model that can make predictions of the response values for a new dataset.
Supervised learning includes two categories of algorithms:
Classification: for categorical response values, where the data is discrete
Regression: for continuous-response values
Discrete data can only take particular values. There may potentially be an infinite number of those values, but each is distinct and there’s no grey area in between. Discrete data can be numeric like numbers of apples, but it can also be categorical like red or blue, or male or female, or good or bad.
Continuous data are not restricted to defined separate values, but can occupy any value over a continuous range. Example a person’s height, time in a race, etc.
Examples
Supervised Learning:
Tools