It is no secret that understanding technology and their terms can be difficult. Especially with it always changing and evolving, it is hard to keep up with all the lingo. Knowing the basic machine learning terms will help you have a better understanding of the technology. Here are the top terms you should know:
Unsupervised learning refers to systems that rely on clustering algorithms. The main goal of this is to allow big systems or computers to a wide variety of information so they can learn and adapt to it. The downside to this is that each system needs to be programmed in order to use the information.
Robotic Process Automation (RPA)
This type of automation is simply using bots to carry out certain tasks. RPA’s work best in an environment that repeats certain tasks.
Linear regression is a statistical algorithm that uses variables to predict values of dependent variables. The main idea of linear regression is being able to use independent and dependent variables to plot data on a line.
Artificial intelligence is becoming more popular in the technology industry, and deep learning is a new approach to AI. Deep learning groups learning algorithms based on hierarchy. For example, the first layer of deep learning can consist of an algorithm based on color and as the layers go up, the level of the algorithm increases due to how important it is to the full algorithm.
Classification is grouping objects, ideas or information into categories. This can be useful for any type of algorithm or machine learning. It keeps track of data and allows the user to select certain information or objects to send out or edit.
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