Machine learning is a type of artificial intelligence, where we somehow make a computer learn how to solve a problem, without having to explicitly program the machine to solve the problem. In other words, the computer learns how to solve a problem that we could not otherwise develop a program to solve.
Machine learning is extensively used today: a Website that tells you what you should pay for a used car, digital assistants such as Siri or Alexa, streaming services that recommend media you might like, and self-driving cars are all examples we use on a daily basis.
Broadly speaking, machine learning can be applied to two types of problems: regression and classification.
In a regression problem, we want the computer to predict some value, based on a set of inputs. The first lesson in the workshop is an example of a regression problem, where we try to predict the attendance rate for a school, based on a variety of input variables.
In a classification problem, we try to take some related data about an event or object and try to determine which class of event or item the data instance belongs to. The second lesson in the workshop is an example of a classification problem, where we try to classify iris plants based on the measured sizes of their petals.
Ready to start? Great! Here is a link to the first lesson.