Machine learning is the process of feeding information to computer systems so that they can learn how to solve problems and predict events like humans do.

Often mistaken for artificial intelligence, which is the broader concept of machines becoming intelligent, machine learning is a direct application of AI that involves giving machines access to data that they can learn from without human interaction.

Machine learning can be integrated into big data processes, enabling businesses and enterprises to teach their machines how to glean insightful information from massive amounts of data that would be impossible for humans to analyze.

Examples of machine learning could range from filtering through mass amounts of data to find the sales leads with the highest potential, to building a system that can better predict fraud and money laundering by examining characteristic patterns within the data set.

There are two common categories of machine learning: supervised, which teaches by example, and unsupervised, which teaches by pattern.

Supervised machine learning relies on large amounts of labelled, annotated data. Using the anti-money laundering example from above, supervised computer systems would analyze millions of transactions that are found to be fraudulent.

Once they find similarities with fraudulent incidents and certain elements of a data set, these computer systems will be able to better predict which cases need a closer examination.

The drawback of supervised machine learning is that it can take extensive data to instill systems with the knowledge they need to make these accurate predictions.

On the other hand, unsupervised learning consists of analyzing and categorizing large amounts of unlabelled data.

For example, the data may consist of millions of files, but since they’re not labelled, it’s impossible to know of what is being shown. However, these files can still be sorted based on a variety of different factors, including number range, file type and more.

Unsupervised machine learning is considered true artificial intelligence, as many of the parameters that it uses to categorize wouldn’t be considered by most humans. As a result, this method of machine learning often solves problems that humans couldn’t understand.

Machine learning can be used in IoT applications to detect information anomalies, predict future events, analyze collected data and more. Both of these innovative technologies are featured in predictive maintenance, one of the most popular machine learning solutions.

IoT-connected sensors can collect information on equipment’s temperature, voltage, audio frequency and more. Once this data is gathered, machine learning can be used to reference existing historical data to make predictions.

If a certain temperature reading is consistent with equipment that fails in the following week, technicians can be alerted, resulting in repairs that can save a business from purchasing brand new equipment.

Resource management is another perfect application for both machine learning and IoT technology. IoT-connected sensors collect information on soil moisture and air humidity, which is then used to create a predictive model that specifies how much fertilizer and water is necessary.

Combined with IoT technology, machine learning greatly benefits smart agriculture by providing additional resource efficiency and analytics that better inform farmers’ decisions.
What makes machine learning unique from traditional computer programs is that a developer hasn’t coded the response that machine learning generates. Instead, machine learning analyzes mass amounts of data to make accurate predictions.