An overview of machine learning models

Machine learning is a subfield of artificial intelligence that explores how machines can learn from data to analyze structures, help with decisions, and make predictions. In 1959, Arthur Samuel defined machine learning as the, "Field of study that gives computers the ability to learn without being explicitly programmed."

A wide range of applications employ machine learning methods, such as spam filtering, optical character recognition, computer vision, speech recognition, credit approval, search engines, and recommendation systems.

One important driver for machine learning is the fact that data is generated at an increasing pace across all sectors; be it web traffic, texts or images, and sensor data or scientific datasets. The larger amounts of data give rise to many new challenges in storage and processing systems. On the other hand, many learning algorithms will yield better results with more data to learn from. The field has received a lot of attention in recent years due to significant performance increases in various hard tasks, such as speech recognition or object detection in images. Understanding large amounts of data without the help of intelligent algorithms seems unpromising.

A learning problem typically uses a set of samples (usually denoted with an N or n) to build a model, which is then validated and used to predict the properties of unseen data.

Each sample might consist of single or multiple values. In the context of machine learning, the properties of data are called features.

Machine learning can be arranged by the nature of the input data:

• Supervised learning
• Unsupervised learning

In supervised learning, the input data (typically denoted with x) is associated with a target label (y), whereas in unsupervised learning, we only have unlabeled input data.

Supervised learning can be further broken down into the following problems:

• Classification problems
• Regression problems

Classification problems have a fixed set of target labels, classes, or categories, while regression problems have one or more continuous output variables. Classifying e-mail messages as spam or not spam is a classification task with two target labels. Predicting house prices—given the data about houses, such as size, age, and nitric oxides concentration—is a regression task, since the price is continuous.

Unsupervised learning deals with datasets that do not carry labels. A typical case is clustering or automatic classification. The goal is to group similar items together. What similarity means will depend on the context, and there are many similarity metrics that can be employed in such a task.