What is Machine Learning?

2023-08-16 | By Maker.io Staff

What is Machine Learning? Source: https://pixabay.com/illustrations/artificial-intelligence-author-7778032/

Machine Learning and AI (Artificial Intelligence) are two of the most popular buzzwords today. ‎However, many people may need help understanding the definitions of these terms, and not ‎everything labeled machine learning uses this powerful computational concept. This article will ‎provide you with an overview of machine learning and some of the most commonly used ‎terminology in ML.

What Even is Machine Learning?

Machine Learning describes an approach to developing algorithms and statistical models that enable machines to make decisions and predictions based on previously collected data samples — all without being explicitly programmed to do so beforehand. So, instead of telling a computer to output a specific value X when the machine observes some input value Y, the machine uses previously supplied data samples to learn patterns in the values and infer that it should output X when it observes Y.

Rather than focusing on specific implementations, machine learning aims to train machines to ‎detect data patterns and relationships. The focus is then on applying that knowledge to new, ‎never-seen-before data samples to calculate output values.

Supervised vs. Unsupervised vs. Reinforcement Learning

There are three main groups of machine learning methods: supervised, unsupervised, and ‎reinforcement learning. These differ mainly in what information is encoded in the data used for ‎training the ML model. In supervised learning, the data contains the input values and the correct, ‎expected output for each sample. In unsupervised learning, these output values, also referred to ‎as labels, are missing.

What is Machine Learning? In reinforcement learning, the model constantly receives feedback from its environment, rewarding correct predictions and punishing incorrect ones.

In contrast, reinforcement learning fits neither of these descriptions. Here, the model receives ‎constant feedback from its environment, either punishing wrong behavior or rewarding correct ‎predictions.

Types of Machine Learning Problems

Generally speaking, you can break down almost all machine learning problems into either classification or regression problems.

In classification, the output of the ML model is a discrete value, meaning the output label — or the result of each calculation — must be one value of a finite set of possible labels. That set could be binary, for example, or contain many labels. The machine must decide which label it assigns to the sample for each input. Examples of classification problems could be weather forecast systems or an image detector that determines whether the image contains a cat, a dog, a horse, or a mouse.

The output is a continuous value in regression problems, meaning it’s a number that could have ‎an arbitrary value. Think of a temperature forecast system that estimates the temperature on ‎any given day in the future based on weather data collected over the last twenty years. Instead ‎of a limited set of labels, the output could be any number, such as 75.3°F.

Summary

Machine learning, often abbreviated as ML, is a programmatic and mathematical approach that ‎teaches machines to recognize patterns and relationships in previously collected data samples. ‎This information is then utilized to make predictions about new, never-before-seen data samples. ‎It’s important to note that the decision-making process is never explicitly programmed. Instead, ‎the model must learn it by interpreting the data samples.

The three main ML approaches are supervised, unsupervised, and reinforcement learning. ‎These differ in how the data supplied to the model during learning is labeled and how the model ‎receives feedback when making classifications.

Further, there are two main problems commonly solved using machine learning — classification ‎and regression. In classification, the ML model assigns a label to every new observation it makes. ‎These labels must be of a finite set of possible values. In regression, the machine calculates a ‎continuous value based on its input.