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What is machine learning (ML)?

Machine learning enables computers to learn from data and improve their performance over time. How can your team leverage this to get more from your data and improve decision-making?

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Machine learning: Definition

Machine learning is defined as the process of using data algorithms to help a computer learn without direct input. It is a subfield of artificial intelligence (AI) that gives computers the ability to learn and reason the way a human brain would, and automatically learn and improve from the data it is fed.

Machine learning uses algorithms and statistics to make classifications or predictions, leading to key insights that drive decision making.

How can machine learning be applied in the organization?

Machine learning is embedded in an array of business applications and software. It is commonly used in search engines, emails for spam filters, banking software for fraud detection, chatbots and apps in the form of speech recognition and predictive text. It can also be used for security purposes like analyzing email communications or internet usage. Organizations can benefit from machine learning as well with process automation — freeing up time and resources.

Why is machine learning crucial for organizations of the future?

Machine learning gives organizations insight into customer trends and operational patterns, and supports the development of new products. The adaptability of machine learning makes it a great choice in scenarios where data is constantly evolving, client requests are always shifting and coding could be complicated.

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The difference between deep learning and machine learning

Machine learning is more dependent on human input to determine the features of structured data. It is an application of artificial intelligence that includes algorithms that analyze and study data, and then apply what it has learned to make informed decisions.

As the artificial intelligence consumes data over time, its capabilities are greatly enhanced and refined.

In deep learning, the artificial intelligence structures algorithms in logical layers.

This “artificial neural network” is capable of learning and making informed decisions on its own. It automates the feature extraction piece of the process, eliminating the need for human intervention and enabling the use of larger data sets. It can analyze raw data, like unstructured documents and images, and determine what distinguishes it from another category of data.

Machine learning and deep learning are interchangeable, as they are all sub-fields of AI, but deep learning is a sub-field of machine learning. The way each algorithm learns is what differentiates machine learning and deep learning. Machine learning requires human intervention to get better, while a deep learning model can improve based on its neural network.

What is supervised and unsupervised machine learning?

Diving deeper, here are two main types of machine learning and how they differ from each other:

  • Supervised learning: This type of machine learning applies when labeled data is used to teach algorithms to predict future events. Supervised learning requires a data scientist to oversee the process as the computer receives data, and the scientist’s input is necessary to validate the artificial intelligence’s decisions. 
  • Unsupervised learning: This applies when data used to train is neither classified nor labeled. Unsupervised learning studies patterns in the unlabeled data so it can draw inferences and create a structure from it. Its ability to discover similarities and differences in data makes it beneficial for operations like data analysis, customer segmentation and image and pattern recognition.

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The benefits of machine learning

Organizations that actively use machine learning have reaped many benefits — with even more possibilities for applications and systems to integrate machine learning as time goes on. Here are some key benefits:

  • Improves data integrity: It is efficient at data mining and constantly improves its abilities over time.
  • Enhances customer experiences: It enhances the customer experience with services such as chatbots and voice-enabled virtual assistants.
  • Reduces risk of fraud: It identifies and captures fraud attempts before they cause major damage.
  • Lowers operational cost: It eliminates and automates manual processes. For example, Hyland RPA allows teams to focus on more important work by automating repetitive, predictable tasks to accelerate reviews and approvals. 
  • Anticipates customer behavior: It helps identify patterns and behaviors to help optimize customer services.

Explore Hyland for process automation