Basic ML Terms You Should Know

By Margaret Rouse
Published: January 3, 2022 | Last updated: January 4, 2022
Key Takeaways

Of these basic ML terms you should know, how many were already familiar? 

Before reading this, why not challenge yourself with our quiz, then come back and check if you were right!


Want more? Click on the link for a fuller explanation of the term.

1. Linear Regression

Linear regression is a statistical model and supervised machine learning algorithm that uses independent variables to predict the value of a dependent variable.


Linear regression is a kind of statistical analysis that attempts to show a relationship between two variables. Linear regression looks at various data points and plots a trend line. Linear regression can create a predictive model on apparently random data, showing trends in data, such as in cancer diagnoses or in stock prices.

2. Deep Learning

Deep learning is an iterative approach to artificial intelligence (AI) that stacks machine learning algorithms in a hierarchy of increasing complexity and abstraction.

The first layer of a deep image recognition algorithm, for example, might focus on learning about color patterns in training data, while the next layer focuses on shapes. Eventually, the hierarchy will have layers that focuses on various combinations of colors and shapes, with the top layer focusing on the actual object being recognized.

3. Classification

Classification is the process of programmatically identifying and grouping objects or ideas into pre-determined categories.


In machine learning (ML), classification is used in predictive modeling to assign input data with a class label. For example, an email security program tasked with identifying spam might use natural language processing (NLP) to classify emails as being "spam" or "not spam."

4. Unsupervised learning

Unsupervised learning is type of machine learning that relies on clustering algorithms such as K-Means.

It is a method used to enable machines to classify both tangible and intangible objects without providing the machines any prior information about the objects. The things machines need to classify are varied, such as customer purchasing habits, behavioral patterns of bacteria and hacker attacks.

The main idea behind unsupervised learning is to expose the machines to large volumes of varied data and allow it to learn and infer from the data. However, the machines must first be programmed to learn from data.

5. Robotic Process Automation (RPA)

A vendor might label software that’s designed to complete discrete workflow tasks programmatically as Robotic Process Automation (RPA).

RPA is a technology that uses software agents (bots) to carry out routine clerical tasks without human assistance. RPA is useful for automating business processes that are rules-based and repetitive.


Share This Article

  • Facebook
  • LinkedIn
  • Twitter

Written by Margaret Rouse

Profile Picture of Margaret Rouse

Margaret is an award-winning technical writer and teacher known for her ability to explain complex technical subjects simply to a non-technical, business audience. Over the past twenty years her explanations have appeared on TechTarget websites and she's been cited as an authority in articles by the New York Times, Time Magazine, USA Today, ZDNet, PC Magazine and Discovery Magazine.

Margaret's idea of a fun day is helping IT and business professionals learn to speak each other’s highly specialized languages. If you have a suggestion for a new definition or how to improve a technical explanation, please email Margaret or contact her on LinkedIn or Twitter.

Related Articles

Go back to top