# What is Machine Learning?

DevFeed: [What is Machine Learning?](<https://devfeed.tech/articles/what-is-machine-learning-28194.md>)

Original publisher: [Read original article](<http://fuzzyblog.io/blog/machine_learning/2020/01/14/what-is-machine-learning.html>)

Author: Fuzzygroup

Published: 2020-01-14T00:00:00Z

Content type: tutorial

Language: en

Sources: [Scott Johnson](<https://devfeed.tech/sources/scott-johnson.md>)

Topics: [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Algorithms](<https://devfeed.tech/topics/algorithms.md>), [data](<https://devfeed.tech/topics/data.md>), [Training AI Models](<https://devfeed.tech/topics/training-ai-models.md>)

Tags: [algorithm](<https://devfeed.tech/tags/algorithm.md>), [data](<https://devfeed.tech/tags/data.md>), [explainability](<https://devfeed.tech/tags/explainability.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [neural-network](<https://devfeed.tech/tags/neural-network.md>), [training-data](<https://devfeed.tech/tags/training-data.md>)

## AI overview

This tutorial defines machine learning as a technology and as the study of algorithms and statistical models that learn from labeled training data to perform tasks or make predictions without explicit instructions. It explains the roles of corpora, labeling, training data, data-driven pattern recognition, and neural networks.

## Source excerpt

I have written a number of posts about different aspects of Machine Learning but, it occurs to me, not a single one that actually says "Machine Learning is xyz and here are the definitions" so here goes. Machine Learning is a technology that allows a computer ("the Machine") to take a "corpus" of "labeled" "training data" and then "learn" how to do something that the training data teaches the computer. So the above is my definition but let's look at Wikipedia: Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task.[1][2]:2 Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop a conventional algorithm for effectively performing the task.Wikipedia And, while it is a simplification, the underlying thing idea driving machine learning is that of data driven pattern recognition. Basically the Machine Learning process amounts to: Take a bunch of data and construct a set of examples that illustrate what you want the machine to learn. This is called Training Data and the overall collection amounts to a Corpus. Add metadata to these examples in a way that represent what you want the machine to learn. Examples could be blocks of text, images, video files or whatever. I personally work mostly in text but I have machine learning buddies who do things learning on images of cancerous masses. This is called Labeling. Labeling might consist of different things like adding Yes / No / Abstain values to a tweet or even extracting information from a recipe like the ingredie