Machine learning is a branch of artificial intelligence (AI) where computer programs and applications can learn patterns through algorithms and training data. Depending on the training data provided to the algorithm, the machine learning software can detect data, make predictions and learn how to improve from continuous learning, creating the ability to complete tasks automatically.
There are three types of machine learning, which are:
- Supervised learning. This type of machine learning is when known or labeled data is used for the training data. Because the training data is known, the learning can be directed, or supervised, to perform the desired outcome. After the machine learning algorithm is trained, it can take unknown data and determine what to do with it based on the data it was trained with.
- Unsupervised learning. Unlike supervised learning, unsupervised learning is trained with data that is unknown and unlabeled. Because the data is unknown, it cannot be guided to perform a specific, desired outcome. Rather, the machine learning algorithm searches for recognizable patterns to produce a result.
- Reinforcement learning. Finally, reinforcement learning is when machine learning algorithms discovers data through a process of trial and error. This type of machine learning is made of three major components known as the agent, the environment and the actions. The agent learns in the environment and chooses actions that will generate the highest reward.
Today, one of the most popular machine learning programs is Alexa from Amazon Echo. When someone asks Alexa to play their favorite song or music station, the software analyzes the user’s listening habits, such as most played stations and most skipped songs, and starts playing music based on that data. Other examples are the software in Google’s self-driving car and recommendations from Facebook and Netflix.
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Machine learning is an incredibly important and useful tool to have in the modern age. Companies are relying more and more heavily on the data they receive from their consumers to make decisions and keep ahead of the competition. Data is a strong driver in displacing competition and meeting consumers needs as they learn more about them. Machine learning is a tool that can make use of this data to further consumer experiences and assist in modern predictive technology.
While AI is a decision-making tool focused on success, machine learning is more focused on a system learning new things from data and having accuracy from that data.
Machine learning versus artificial intelligence
The most basic difference between machine learning and artificial intelligence is in the name. Artificial intelligence is implementing a human-like intelligence into a technological system. Machine learning is a type of AI and is when a machine can learn patterns, trends, etc., on its own without being explicitly programmed to do this learning. While AI is a decision-making tool focused on success, machine learning is more focused on a system learning new things from data and having accuracy from that data.
Benefits of machine learning
There are several benefits to machine learning, including:
- Trend identification. As machine learning processes data from consumers, the results can provide valuable insight into what customers are purchasing and how they’re using these products or services. These trends can then be used to make important decisions in a business or corporation.
- Task automation. Because machine learning can detect and process data, employees can be freed from manually inputting data into a computer. Machine learning removes human error and gives employees back more time to focus on growing business products or services.
- Protection from cyber fraud. Using machine learning in recognizing phishing emails or online scams with potential fraud creates an extra layer of security for businesses.
- An increase in sale opportunities. Analyzing new, existing and returning consumer experiences using machine learning programs can identify new sales trends and refine the profiles of consumers to cater to their needs as well as find ways to address target audiences and gain new followers.
- Improvement in customer satisfaction. As machine learning programs can be used to identify trends based on personal feedback data from consumers, decisions can be made in businesses to cater to personalization, creating greater satisfaction from consumers.
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Machine learning can seem confusing and highly technical. However, learning the ins and outs of machine learning and how to utilize this convenient tool can assist with your business, customer satisfaction, and defer competition.
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