Machine Learning (ML) provides businesses with deep insights into customers and operations. Businesses can implement machine learning to more efficiently manage, develop and enhance their capabilities, which can provide a significant advantage in competitive markets. 

ML enables computer systems engaging in data-related applications to learn from data in real time by employing statistical tools and techniques. It allows businesses to capitalize on their data in a variety of applications.

ML incorporates machine learning algorithms, which are formal statistical methods and processes, to drive intended outcomes and generate predictions or forecasts. These algorithms are a series of mathematical steps to solve a data-related problem. There are many different ML algorithms, each of them suitable for specific scenarios and applications. They generally fall into the following categories:

• Supervised Learning: Algorithms train themselves using labeled and well-defined data where the expected output is known. The algorithm applies statistical methods and processes to make predictions based on the input data. Conventionally, the algorithm’s results can be compared to the actual known results and adjustments can be made before the next iteration of the algorithm. The process can be repeated until the desired results achieve an acceptable level. 
• Unsupervised Learning: Algorithms train themselves using unlabeled data where the expected outcome is not known. The algorithm applies statistical methods and processes to identify patterns, correlations, and relationships that may be hidden or difficult to perceive in a given data set. Typically, unsupervised machine learning algorithms will organize and group data in some meaningful way.
• Semi-supervised Learning: Algorithms train themselves using a combination of labeled and unlabeled data. The algorithms apply a small set of labeled data to identify meaningful information that can be used to understand a larger set of unlabeled data.

A comprehensive understanding of business context and data systems is critical when leveraging machine learning algorithms to solve problems. The algorithm implemented directly impacts the quality of results that can be obtained from your data. Therefore, applying the optimal algorithm is critical when implementing machine learning. AIPI3® offers solutions specifically focused on machine learning algorithms: We design, build, and tailor algorithms to your business need to ensure optimal results.