Two definitions of Machine Learning to understand the concept.
"the field of study that gives computers the ability to learn without being explicitly programmed." By Arthur Samuel.
"A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E." by Tom Mitchell.
The term Supervised Learning refers to the process of giving data sets and the expected "right answer" to an algorithm. The task for the algorithm is then to produce a model that matches the right outputs for each data set.
In this process the algorithm learns to shape a model by comparing its outputs to the one given by a human. It is called supervised learning because the process of an algorithm learning from the training dataset can be thought of as a teacher supervising the learning process.
Supervised learning algorithms problems can be grouped into two main categories:
Unsupervised learning allows to approach problems without knowing the expected output. Algorithms derivate structure from a given data set without humans knowing how results will be. They are called unsupervised learning because there is no feedback based on the prediction results nor correct answers.
Unsupervised learning algorithms problems can be grouped into two main categories:
Easy steps to configure a Git repository and change the Git user name and email. A review of git's config object and how to retrieve information from a git repository using the git config command.
A high overview of Virtual Power Plants. How these centralised IT systems are allowing to control multiple units generating energy from a centralised Dashboard panel.
What is Domain Driven Design and why it is important to define a clear terminology? This article covers the key aspects of developing software following best practices of Domain Driven Design.