The Top 9 Machine Learning Key Terms You Need to Know
AI and machine learning have become more prominent than ever in recent years. This has allowed businesses to accomplish tasks that were previously thought to be impossible.
Integrating this technology into your organization is also easier than you think. Before you get started, there is a handful of machine learning key terms that you will need to know.
Machine Learning Terms:
This process allows users to predict a specific variable accurately. It uses one or more variables to help deduce this value.
This allows you to minimize the chance of errors occurring. In practice, machine learning that uses a regression model builds a mathematical equation that defines y as a function of other variables (here defined as x).
As the name suggests, classification refers to a situation where a class is predicted based on input data. This is highly useful in many different scenarios, such as filtering spam emails.
Classification also makes use of decision trees. This is a mechanic of the program that answers numerous consecutive questions in order to reach a solution.
For example, let's assume that a classification model is used to help determine whether or not you should hire a particular employee.
The decision tree could include information about their experience, estimated desired salary, etc. Of course, the applications for classification are virtually endless.
In order to better analyze data, it's often in your best interest to segment it into different groups. Clustering aims to do this automatically.
It accomplishes this goal by learning to recognize natural groups within a particular dataset. From here, it will be much easier for you to review this information and develop a more robust understanding of it.
Without clustering, you would have to perform this process manually — something that is often highly time-consuming and inefficient. When implemented correctly, clustering can segment data that would be impossible to work with by hand.
4. Reinforcement Learning
As an entrepreneur, you want to ensure that you always take the optimal action for the growth of your business. Reinforcement learning aims to analyze a particular situation and determine the most beneficial decision.
However, this machine learning process isn't limited to difficult decisions. It can also be used to help make simple decisions more efficiently.
This allows the software to provide much better results. In a business context, this could easily translate to a significant increase in revenue.
5. Deep Learning
This term actually refers to a subfield of machine learning. Deep learning makes use of algorithms that attempt to mirror the function of the human brain.
These are also known as artificial neural networks.
Essentially, the algorithms are a matrix of nodes the are used to help understand problems and formulate a solution.
It should come as no surprise that the potential applications of deep learning are fairly diverse. Additionally, this technology becomes more efficient over time.
Put simply, deep learning is a highly powerful tool that your business could take advantage of. It allows you to quickly gain insight into difficult concepts and formulate a comprehensive analysis.
So, it's something that shouldn't be overlooked.
6. Structured Data
Data that is formatted into a specific structure is quantitative. This means that it is displayed as numerical values, such as dates.
It's also stored in rows and columns, similar to what you would find in a spreadsheet.
The primary purpose of structured data is to provide insight into trends that are occurring. From here, you can analyze this information in order to determine the best action to take.
Working with structured data is notably straightforward. In fact, most businesses use spreadsheet programs like Microsoft Excel in order to manage this type of information.
7. Unstructured Data
On the other hand, unstructured data is qualitative. Content like audio, images and video fall into this category.
As you may expect, the majority of data found within organizations could be considered unstructured. Similar to structured data, unstructured data aims to understand why something is occurring within a trend.
This data is particularly difficult to analyze on a large scale without using AI. So, machine learning is being used more frequently for this purpose.
A common example could involve mass facial recognition within a large number of photos.
8. Model vs. Model Artifacts Training
In machine learning, a model is a file that has been specifically trained to recognize certain patterns. In practice, this involves providing it with an algorithm that it can use for logical reasoning and data analysis.
A model artifact is a specific output from training a machine learning model. In some cases, the output of a model could be an entirely separate model.
More often than not, though, it is simply an additional file that is generated based on the function of the initial model.
A machine learning inference consolidates live data points into a single machine learning algorithm. It then calculates a single output that is displayed as a numerical value.
From here, this information can be added to a dataset, analyzed further with additional machine learning tools, etc.
The Above Machine Learning Key Terms Are Essential to Know
Fortunately, they are relatively easy to understand. Keep the above information in mind about these machine learning key terms so that you can develop a solid idea of how to integrate this technology into your business.
Want to learn more about what machine learning can offer your organization? Feel free to get in touch with us today and see how we can help.