Machine learning — definition, application, and methods

A laptop screen shows data about machine learning.

Artificial intelligence and machine learning are technology buzzwords floating around in business and popular discourse, but they aren’t the easiest concepts to understand. While you’ve no doubt heard of both, and the two terms are often used interchangeably, they are not the same.

Machine learning (ML) is an application — and the most well-known type — of artificial intelligence (AI), used in everything from self-driving cars to marketing software. Forward-minded companies are increasingly using ML to innovate, improve processes, increase efficiency, and more. So, modern business leaders need a thorough understanding of this emerging technology to take advantage of its capabilities.

That starts with fully grasping machine learning, knowing why it’s important, and understanding the different ML models. This article will provide a detailed definition of machine learning and explain how it works and how it relates to AI.

Specifically, we’ll explore:

What is machine learning?

Machine learning is a subset of artificial intelligence involving computer models that improve their performance when given more data. ML uses algorithms to analyze data and make decisions with minimal direct inputs and without explicit programming or human intervention.

This technology allows machines to “learn” from experience and is invaluable in surfacing insights from vast amounts of data in ways that would otherwise be difficult or impossible. ML can provide incredible advantages in time savings, productivity, and cost to organizations.

How does machine learning work?

Machine learning operates under the same basic model of learning as the human brain — it gathers information through inputs and finds ways to make sense of it to inform future analysis. ML uses information like training data and knowledge graphs to make sense of categories of information and their relationships. As machines define these entities and connections, they develop the capacity for even deeper learning.

Machines start the learning process by observing and gathering data through direct experience, instruction, demonstration, or example. They look for rules or patterns that give them a sense of the organization for the information so they can make deductions and inferences based on these patterns in the future. With this ability to understand categorizations and detect patterns from data, machines develop the capacity to learn on their own and increase their accuracy through informed automatic adjustments.

When machines are learning, it’s important their inputs consist of accurate, high-quality data. Poorly gathered or low-quality data will lead to inaccurate models and subpar results. It’s also important to consider which algorithm works best for any given task — specific outcome needs will play a primary role in determining which algorithms are most suitable.

Some algorithms are more effective with specific types of data and will provide different levels of accuracy. This is why it’s important to have an understanding of data quality and intended outcome before selecting a machine learning algorithm.

ML is a subset of AI involving computer models that improve their performance when given more data. It uses algorithms to analyze data and make decisions with minimal direct inputs and without explicit programming or human intervention.

Why is machine learning important?

Machine learning can solve problems at a speed and scale beyond what humans are capable of due to its immense processing power. ML can provide vital business services faster and cheaper than traditional analytics teams can, from enabling risk mitigation to providing 360-degree customer views to supporting product innovation and iteration.

The speed, cost, and quality advantages that these data-driven insights and capabilities provide can mean the difference between staying on top of market trends and being left behind.

Machine learning applications

Machine learning can be used in a variety of applications. It can help leaders make business-critical predictions with market and performance analytics. It can provide risk mitigation support through anomaly and fraud detection, generating and sharing alerts quickly when something suspicious or unusual happens. These high-value contributions are already being put to work in a variety of businesses and sectors.

Here are some ways ML is currently used:

Data security. Machine learning algorithms can recognize patterns in large volumes of information in order to detect malware and phishing attacks, alert people to suspicious activity, or detect unauthorized access to software, systems, and data. This kind of analytic ability enables organizations to respond to potential threats quickly and accurately.

Finance. The finance industry deals with enormous amounts of data and uses machine learning to reduce risk while increasing growth. Applications include trading strategies, portfolio optimization, market and trends analysis, and risk management and mitigation. Financial analysts use ML algorithms to help identify and mitigate risk by discovering patterns in historical data and predicting future events that could cause losses.

Healthcare. Machine learning is used in diagnostics due to its ability to recognize patterns undetectable to the average person. For example, ML can aggregate and analyze data in X-rays or photographs to determine whether the images point to cancer or other unusual growth patterns. It also can be used to analyze complex symptoms or investigate samples that can help a doctor diagnose disease more accurately.

Fraud detection. Through the use of sophisticated algorithms, machine learning can identify suspicious activity faster and more accurately than traditional methods. This helps to reduce losses due to fraud and maintain the security and integrity of data and systems. ML algorithms are trained with historical data to surface vulnerabilities and suggest rules to address them — the longer the algorithms run, the more accurate these suggestions will be.

Retail. Retail organizations use ML to enhance business and customer experience. Predictive analytics can help retailers better understand real-time consumer behavior and preferences so they can provide more relevant campaign material, optimize their end-to-end operations, and create personalized experiences for those customers. Retailers that use machine learning can see improved customer engagement, increased sales, and higher ROI.

Machine learning methods

Many different kinds of algorithms have been developed to help solve complicated problems in real-world scenarios. These algorithms are programmed to self-correct based on what they “learn” from iterative data inputs so that they can modify and optimize themselves over time.

There are four main types of ML algorithm classifications — supervised, unsupervised, semi-supervised, and reinforcement.

There are four main types of ML algorithm classifications — supervised, unsupervised, semi-supervised, and reinforcement.

Supervised learning

Supervised learning enables machines to make predictions from labeled data. This process involves entering labeled datasets into an algorithm, which machines can use to identify patterns in the data and build models based on the patterns. Supervised learning is often used for classification tasks like identifying objects in images or text recognition and works best when the appropriate outputs to inputs are known.

Unsupervised learning

With unsupervised learning, there are no labels or outcomes provided in the dataset. The algorithm has to deduce labels, categories, and other structural elements from the data itself, and it produces insights without human intervention. Unsupervised learning is used when data does not have structured or objective answers, such as defining customer groups that display similar behaviors or preferences.

Semi-supervised learning

Semi-supervised learning combines both supervised and unsupervised learning approaches. It provides a reference or baseline for a ML model with unlabeled data, then uses unlabeled data to correct and fine-tune the predictive accuracy of the model. Semi-supervised learning approaches work well in situations where access to labeled data is limited, such as text classification or speech analysis tasks.

Reinforcement learning

Reinforcement learning helps machines learn through a kind of controlled experimentation. Machines act or perform in certain environments and then learn through natural corrective feedback and consequences. Reinforcement ML has a wide variety of applications, including in the development of self-driving cars, energy consumption prediction, robotics, and gaming.

Regardless of the type of machine learning algorithm that is used, they all have the same goal — to perfect predictive accuracy in a model based on iterative inputs and training data.

Improve customer experience with machine learning

As businesses discover the vast and powerful applications for machine learning, they can use the technology to engineer more efficient internal processes and capture more accurate and timely customer information. They can then use the insights and efficiency to provide up-to-the-minute personalization.

Machine learning is a powerful tool smart leaders are already using to gain a competitive market advantage. When you’re ready to use machine learning in your own business applications, start by evaluating which of your processes can benefit most from the kinds of automation and customer data processing ML provides.

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