Machine Learning (ML) has transformed the world of technology as we know it. ML algorithms are responsible for advancements in artificial intelligence and the applications that rely on it. Machine Learning algorithms also use statistics to find patterns in large amounts of data, making it easier to identify pattern behaviors. According to Forbes, “The global machine learning market is projected to grow from $7.3B in 2020 to $30.6B in 2024, attaining a CAGR of 43%.” Consequently, the demand for ML is steadily increasing among various industries and companies worldwide.
To get a clearer understanding of how ML works, we must explore the algorithms that work behind the scenes to create a productive ML environment to operate best. Below are four primary ML algorithm types:
- Supervised Learning: Training is a big part of this algorithm, and at the end of the process, a function that best describes this input function is defined. IT experts feed the computer with training data that contains the input and then show the output, leading to the computer learning the pattern. According to Towards Data Science, these algorithms try to “model relationships and dependencies between the target prediction output and the input features…” In other words, the data is labeled and tells the machine exactly what patterns to seek.
- Unsupervised Learning: This method’s data is label-free. Instead, the machine simply looks for patterns on its own. These algorithms attempt to use input data techniques to mine for specific rules, detect patterns and summarize/group data points. Consequently, meaningful insights are collected, and the machine can then describe the data better to users.
- Semi-supervised Learning: This method falls between supervised and unsupervised machine learning. When there is an absence of labels in most patterns but are present in a few, semi-supervised algorithms are the best options to build the model.
- Reinforcement Learning: Uses observations collected from an interaction with the environment to take actions that will intensify the reward or decrease the risk. Reinforcement algorithms consistently learn from its environment. In the process, the algorithms learn from its experiences until it has successfully explored the range of possible states. The Towards Data Science article states, “simple reward feedback is required for the agent (algorithm) to learn its behavior; this is known as the reinforcement signal.”
These four algorithms are the prominent masterminds behind ML and its ability to function within a system correctly. Machine learning is a process that focuses on data patterns, and these algorithms have individual roles that are unique to an environment’s needs.
Next Steps: When to Use Algorithms
Now that we have covered our necessary need-to-know information, you still might be wondering how to put this knowledge into practice. Well, the key to understanding when it is appropriate to use either algorithm depends on a few factors that include:
- Size of data
- Quality of data
- Nature of data
- The urgency of data needs
Whether you are new to ML or a veteran, it is essential to keep these factors in mind because they will save you time and effort. If your credit union’s IT team has a security and compliance platform, this work is already done for you. Incorporating machine learning into your organization‘s security needs is an integral part of ensuring your credit union’s networks are continuously protected against all cyber threats.