Why is machine learning important?

The resurgence of interest in machine-based learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. Things like the increasing volumes and varieties of data available, more economical and powerful computational processing, and affordable data storage.

All these things mean that it is possible to produce models quickly and automatically that can analyze larger and more complex data and produce faster and more accurate results – even on a very large scale. And with the construction of precise models, an organization has a better chance of identifying profitable opportunities – or avoiding unknown risks.

Who uses it?

Most industries that work with large amounts of data have recognized the value of machine learning technology. By obtaining insights from this data – often in real time – organizations can work more efficiently or gain an advantage over their competitors.

Algorithms are the engines that drive machine learning. In general, two main types of machine learning algorithms are currently used: supervised learning and unsupervised learning. The difference between them is defined by how each one learns about the data to make predictions.

Supervised machine learning

Supervised machine learning algorithms are the most used. With this model, a data scientist acts as a guide and teaches the algorithm the conclusions to be made. Like a child who learns to identify fruits by memorizing them with an image book, in supervised learning, the algorithm is trained by a set of data that is already labeled and has a predefined result.

Examples of supervised machine learning include algorithms such as linear and logistic regression, multi-class classification and support vector machines.

Machine learning unsupervised

Unsupervised machine learning uses a more independent approach, in which a computer learns to identify complex processes and patterns without a human being providing close and constant guidance. Unsupervised machine learning involves data-based training that has no labels or a specific defined outcome.

To continue with the analogy of child education, unsupervised machine learning is similar to a child who learns to identify fruits by observing colors and patterns, instead of memorizing names with the help of a teacher. The child would look for similarities between the images and separate them into groups, assigning each group its own new label. Examples of unsupervised machine learning algorithms include k-means clustering, principal and independent component analysis and association laws.

Choice of an approach

What is the best approach for your needs? The choice of a supervised or unsupervised machine learning algorithm generally depends on factors related to the structure and volume of your data, and the use case to which you want to apply it. Machine learning has flourished in a wide range of industries, offering assistance in a variety of business objectives and use cases that include:

Customer Life Value

Anomaly detection

Dynamic pricing

Predictive Maintenance

Image Classification

Recommendation engines

How does it work

To get the most value from machine learning, you have to know how to match the best algorithms with the right tools and processes. SAS combines a rich and refined heritage in statistics and data mining with new architectural advances to ensure that your models are processed as quickly as possible – even in large business environments.