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What is Machine Learning? Defined, Explained, and Explored

What is Machine Learning?

Machine Learning Defined, Explained, and Explored

Machine Learning

Machine Learning Defined

What do Google’s self-driving car and Netflix’s recommendation offers have in common? They all use machine learning to a degree to make repeatable decisions, perform specific tasks and independently adapt with little to no human interaction.

To define machine learning in very simple terms, it is the science of getting machines to learn and act in a similar way to humans while also autonomously learning from real-world interactions and sets of teaching data that we feed them.

Machine learning is not a new technology. The algorithms that drive today’s pattern recognition and machine learning applications have been around for many years. However, it is only now that machine learning models are starting to interact with more complex data sets and learn from previous computations and predictions to produce reliable decisions and results. Build the right model and you have a better chance of avoiding unknown risks and identifying profitable opportunities across your business.

READ: Do Security Analysts Trust Machine Learning Powered Analytics?

Types of Machine Learning & Common Algorithms

Machine learning is not an exact science. It encompasses a broad range of machine learning tools, techniques and ideas. Here are the most common types of machine learning techniques and algorithms along with a brief summary of how each can be used to solve problems.

Supervised Learning

Some of the most simplistic tasks fall under supervised learning. For example, a handwriting recognition algorithm would typically be classified as a supervised learning task. However, these tasks can only be carried out if the computer is given correct input-output pairs.

Unsupervised Learning

Where supervised machine learning algorithms look for patterns from a dataset of correct answers, unsupervised learning tasks find patterns that are often impossible for a human to identify. For example, a marketing algorithm might use unsupervised learning to identify segments of prospects with similar buying habits.

Reinforcement Learning

Instead of providing the computer with correct input-output pairs, reinforcement learning provides the machine with a method to measure its performance with positive reinforcement. Similar to how humans and animals learn tasks, the machine tries a number of ways to solve a problem and is rewarded with a signal if it is successful. This behavior is then learned and repeated the next time the same problem is presented.

Machine Learning Applications

Of course, all of this technology would be wasted if it wasn’t put to good use. There are many machine learning tools and applications currently in use across every industry. Some of the most common include:

Data Security

Malware is a problem that isn’t going to go away anytime soon. The bad news is that thousands of new malware variants are detected every day. The good news is that new malware almost always has the same code as previous versions. This means that machine learning can be used to look for patterns and report anomalies.

Financial Trading

Patterns and predictions are what help keep the stock market alive and stockbrokers rich. Machine learning algorithms are in use by some of the world’s most prestigious trading companies to predict and execute transactions at high volume and high speed.

Marketing Personalization

When you understand your customers, you can serve them better. When you serve them better, you sell more. Marketing personalization uses machine learning algorithms to create a truly personalized customer experience that is matched to their previous behavior, likes and dislikes, and location-based data, such as where they prefer to shop.

Advanced Compromised Account Detection – UEBA

Machine Learning Tools in Business

Apple, Google, Facebook and Microsoft are just some of the tech giants that are leading the way with machine learning. In June last year, Apple released its Core ML API, which is designed to speed up artificial intelligence on the iPhone. And Microsoft’s Azure cloud services now incorporate an Emotion API that is able to detect human emotions such as sadness, anger, happiness, disgust and surprise.

The thing these tools all have in common is that they are dynamic and able to adapt to new rules, new environments and newly learned information. From recommendation engines to facial recognition, machine learning is leading the way for companies that are dealing in big data and big decisions. In a world where business needs to stay one step ahead of the latest threats, competition and human error, this technology enables organizations to be more agile and reactive than ever before.

In addition to using machine learning to enhance processes and make data-driven decisions, organizations need to be able to manage the security of their data more efficiently and in ways that don’t slow employees down. The answer is Dynamic Data Protection from Forcepoint. Using human-centric behavior analytics and individualized, adaptive data security, Dynamic Data Protection reduces time spent sorting through alerts and allows your DevSecOps teams to be more proactive.


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