To understand everything about machine learning, you need to understand what machine learning algorithm is. In this article, I will explain what machine learning algorithms are.
Machine learning algorithms are a type of statistical techniques applied to data using a programming language to statistically estimate complicated models from the data.
What are Machine Learning Algorithms?
A Machine learning algorithm is an algorithm that specifically learns from data. But how does it learns and how do algorithms work? Let’s take a look at the definition of machine learning algorithm defined by a popular computer scientist, and then I’ll explain each term used in the definition of the machine learning algorithm.
We say that a computer program learns from experience E concerning a certain class of tasks T and a performance measure P, if its performance on tasks of T, as measured by P, improves with experience E.
So we can imagine a wide variety of E experiments, T tasks and P performances. Now let’s understand all these terms to understand what machine learning algorithms are.
Understanding Machine Learning Algorithms:
Machine learning tasks are defined as how the machine learning application should process an example. Here the example is a collection of functionality that has been measured from an object or event that we want to process using the machine learning application.
To assess the performance of a machine learning algorithm, we need to design a quantitative measure. Normally, performance metrics are specific to the tasks performed by the machine learning application.
Choosing the performance metric is difficult because it is very difficult to choose a performance metric that shows the behaviour of algorithms. In most cases, it is difficult to choose because it is very difficult to decide what should be measured.
Machine learning algorithms can be classified as supervised and unsupervised algorithms depending on the type of experiences they get during the learning process. By default, most algorithms are allowed to learn from the entire dataset. A dataset is a collection of entities, where each entity contains examples and the examples are called data points.
Unsupervised algorithms learn from a dataset that contains many factors by understanding the properties of the dataset. Supervised algorithms learn from a dataset containing features, where each example is associated with a target label or class.
Unsupervised and Supervised algorithms are not formally defined. Most of the applications are using both these type of algorithms. I hope you liked this article on what are machine learning algorithms. Feel free to ask your valuable questions in the comments section below.