Machine learning is a subfield of computer science concerned with building algorithms that rely on a collection of examples of some phenomenon to be useful. Artificial intelligence includes machine learning.
Machine learning can also be defined as solving a practical problem by 1) gathering a dataset and 2) algorithmically building a statistical model based on that dataset. That statistical model is assumed to be used to solve the practical problem.
In this article, you will learn What is Machine Learning for Dummies, as well as the types of Machine Learning, what is Machine learning used for, and what are different Machine Learning algorithms? etc. You will learn the same as the “what is machine learning GeeksforGeeks” article teaches you. Continue reading to get the answers to all of your queries!
What is Machine Learning?
Machine Learning is the science (and art) of programming computers so they can learn from data.
Here is a slightly more general definition:
Arthur Samuel states, “Machine Learning is the field of study that allows computers to learn without being explicitly programmed.”
And a more engineering-oriented one:
According to Tom Mitchell, “A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.”
For example, your spam filter is a Machine Learning algorithm that can learn to recognize spam based on samples of spam emails (e.g., marked by users) and samples of normal (nonspam, sometimes known as “ham”) emails. The models that the system employs to learn are referred to as the training set. Each training example is known as a training instance (or sample). In this situation, task T is to detect spam in new emails, experience E is the training data, and the performance measure P must be defined; for example, the ratio of correctly classified emails can be used. This type of performance metric is known as accuracy, and it is frequently employed in classification jobs.
