Machine learning is continually being developed and enhanced to make accurate predictions and in-depth calculations based on large amounts of data. When combined with artificial intelligence tools, machine learning algorithms are the key to unlocking the powerful potential of Big Data. There are three basic types of machine learning algorithms.

Supervised vs. Unsupervised Algorithms

Supervised learning refers to target, outcome or dependent variables that are predicted based on a set of independent factors.  These variable sets map and correlate the inputs to target outputs. This training process continues until the machine model achieves a designated level of data accuracy. Unsupervised learning algorithms do not have variables to predict. Instead, it’s used for clustering categories and populations in assigned groups.

Reinforcement Learning Algorithms

Reinforcement learning falls between the two extremes of supervised and unsupervised algorithms. While there is some sort of feedback available predictive steps and actions, there are no exact labels and error messages. These advanced algorithms train the machine to make specific decisions. That is, the machine is exposed to a controlled environment that allows it to use trial and error to learn from past experience, identify the best knowledge and make accurate decisions.

Algorithm Examples

Supervised learning algorithms include regression, random forest and decision tree models. Unsupervised learning models include K-means and Apriori algorithms. For example, linear regression is used to estimate the real value of items, such as total sales and housing costs, based on continuous variables. Linear regression models are used to establish relationships between dependent and independent variables. On the other hand, logistic regression decides values like true and false based on specific independent variables.

Finally, decision trees are supervised learning algorithms used for classification problems. There are many more complex algorithms that offer unique benefits and specific functions. One of the easiest ways to get started with Big Data and machine learning is to use an online data platform that provides customized solutions.


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