Machine Learning: What It Is, Machine Learning Techniques, Role in Business

It is sometimes mistakenly believed that artificial intelligence and machine learning are the same. In reality, machine learning (ML) is just one component of AI, enabling computers to process large data and solve various tasks by mimicking the human brain. For computer systems to solve tasks of varying complexity, similar to a human, it is necessary to train the computer to analyze vast arrays of data, and machine learning assists in this.

The idea behind machine learning is that just as a human acquires a skill through numerous trainings and perfects their technique through trial and error, the same must be done with a computer. In machine learning, computer systems process data using algorithms capable of structuring information, and the more data they process, the more accurate the result. There are several techniques of machine learning, and their main feature is that the «trainer» is not a human, but the data itself, labeled or not. This includes controlled and uncontrolled learning: in controlled learning, the computer system is offered to process a database already based on the labels or structure embedded in it, while in uncontrolled learning, the computer itself discovers connections and patterns, turning data into clusters.

There is also a third technique — reinforcement learning, which aims to determine how well an algorithm copes with the task. AI and machine learning are also used in business, helping corporations to more clearly see not only the non-obvious and hidden needs of consumers but also flaws in the company’s work processes.

The scope of machine learning application is extensive: banking, healthcare, customer service, agriculture, retail, and much more. Among the capabilities of machine learning used in these areas, forecasting stands out. Value forecasting involves creating a model based on values to get a rough picture of the future, such as profit, product demand, sales indicators. Another type of forecasting is category forecasting: the algorithm must determine the correct information category.

Thus, machine learning can divide data into clusters, significantly facilitating the work of marketers and targetologists, whose task is to offer the right product to the right audience in the right environment. In addition to forecasting, machine learning can train a computer in identifying deviations — data that does not conform to the norm. Thus, machine learning can optimize work in identifying financial and technical risks, defects, errors, and malfunctions — and possibly even prevent a critical situation.

Although the potential of machine learning is not yet fully realized, business is interested in such technologies to increase profits and optimize internal processes.