An algorithm is a step-by-step computational procedure used to solve a problem. It is very similar to decision-making flowcharts that can be used to process information and perform mathematical calculations.
Machine learning uses algorithms to create models to identify patterns in data. Data scientists use feature engineering to improve the performance of these algorithms. Once these patterns are revealed, businesses can make predictions and gain insights that will allow them to make better decisions.
There are three basic types of machine learning algorithms: supervised learning, unsupervised learning, and reinforcement learning.
In supervised learning, there is a target variable that will be predicted from a given set of data. A function is generated that maps inputs to desired outputs and the training then continues until the model is sufficiently accurate. Some examples of supervised learning are regression, random forest, and decision trees.
Unsupervised learning algorithms do not try to predict a target feature, but rather groups data into different segments. K-means and the Apriori algorithm are both examples of unsupervised learning.
A reinforcement learning algorithm trains a machine to make specific decisions. The machine is essentially exposed to an environment where it can continuously train itself using trial and error. This algorithm uses previous experiences to try and make the most accurate business decisions. The Markov Decision Process is considered reinforcement learning.
Artificial intelligence and machine learning would not be possible without machine learning algorithms. These algorithms provide the foundation for machine problem-solving skills and allow us to enjoy the predictive software and technology we rely on daily.
A great example of a machine learning algorithm in our everyday lives is the one that Netflix uses to analyze the shows we watch and make recommendations based on what it thinks we enjoy. Similar algorithms are used by retailers to suggest other products you may enjoy based on your purchase history.
Machine learning algorithms allow businesses to process and analyze big data to help them make informed, strategic decisions.
It doesn’t matter if you only have BI analysts, IT leaders, or executives wanting to leverage algorithms or if you have a team of in-house data scientists looking to be more impactful with their analytic output. Any organization looking to start or advance their AI journey can benefit from algorithms.
LogicPlum is committed to changing the way business and analytics professionals work with machine learning algorithms and artificial intelligence. Our platform improves the user’s ability to consume and analyze data, thus allowing firms to make better business decisions.
We believe that our platform, combined with a human element, will augment results and provide a much better outcome from big data models and machine learning algorithms.
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