According to Gartner, a global IT research company, Machine Learning is the result of various technological combinations — from deep learning to natural language processing, through neural networks and Artificial Intelligence.

To put it more simply, it is a concept that involves machines learning on their own. It encompasses a kind of Artificial Intelligence that enables software and hardware to gain precision at predicting results without having to be explicitly programmed for it.

Keep reading to understand in practice what Machine Learning is, what relationship this concept has with Analytics, and how you can use it in your business!

Learning How Machine Learning Works

Data scientists use many different types of machine learning algorithms to discover patterns in exponential volumes of data. At a high level, they can be classified into two groups based on how they “learn” to make predictions — supervised and unsupervised learning.

Supervised learning

Supervised machine learning is the most commonly used one. It includes algorithms such as linear and logistic regression, classification of several classes, and support vector machines. It is so called because the data scientist acts as a guide to “teach” the machine what conclusions it must present.

This type of learning requires that the possible outputs of the algorithm be already known and that the data used to “train” it be labeled with correct answers. For example, a classification algorithm will learn to identify animals after being parameterized into a set of images that are properly labeled with the species and some identifying characteristics.

Unsupervised learning

On the other hand, unsupervised machine learning is more in line with what some call “true AI” — the idea that a computer can learn to identify complex processes and patterns without a human providing guidance along the way.

While this type of learning is quite complex for some simpler business use cases, it creates paths for solving problems that humans would not normally solve.

Some examples of unsupervised machine learning algorithms include k-means grouping, principal and independent component analysis, and association rules.

No matter the category of the algorithm, Machine Learning processes will always depend on predictive modeling and data mining. That is, they will require searching large volumes of data to find patterns and adjust actions.

In short, the goal of all Machine Learning algorithms is to estimate a predictive model that best generalizes to a particular type of data. Therefore, to solve a problem through machine learning, it is necessary to have a large number of samples that can be used by the algorithm to understand the behavior of the system. Similar types of predictions can be generated when the algorithm is presented with new data samples.

What is the relationship between the concepts of Machine Learning and Analytics?

The concept of Machine Learning has grown especially in the so-called “new economy” companies, those that are undertaking digital transformation strategies — creating virtualized products or services or leveraging them to gain differentiation and scale.

In this movement, the concept closely relates to Analytics, which, in a nutshell, is the process of discovering and communicating the meaningful patterns that can be found in the data. That is, through applications such as Business Intelligence and CRM, organizations can conduct deep analysis of large amounts of data to turn them into useful information for their businesses. This can be done by investing in Contact Centers with the implementation of speech analysis software integrated with “virtual assistant” type applications to detect patterns of interaction and lead relationships more focused on the user/consumer experience.

Thus, by implementing Machine Learning and Analytics tools and methods, companies take an important step toward increasing efficiency, gaining more competitive advantage, and further boosting their bottom line.

Some examples of how Machine Learning is being applied at enterprises

A very clear example of Machine Learning, which is already embedded in our daily lives, are the ads we receive on sites that we navigate after we make a purchase — or even a query — in a virtual store.

The applications behind e-commerce “learn” what our interests are and then customize recommendations. That is, the algorithm “monitors” our browsing history, among other data, and processes the communication and marketing actions preconfigured by the advertisers.

HubSpot, systems developer for Inbound Marketing and Sales, uses machine learning and natural language processing technologies in its internal content management system to better identify triggering events, attract potential customers, and serve current ones.

TrademarkVision, the world leader in image recognition, uses Machine Learning on its platform to determine if a new logo is acceptable or if it violates existing trademarks.

In addition to these typical marketing and sales applications, there are already organizations using Machine Learning to intelligently detect fraud in virtual stores, spam filtering in email boxes, detection of security threats in computer networks, predictive maintenance of the infrastructure, etc.

What are the practical benefits of a Machine Learning strategy?

A recent survey conducted by MIT Technology Review in conjunction with Google Cloud shows that machine learning has become an IT priority for many large US organizations. The study states that 60% of those organizations are already using this concept in some way.

Among the advantages observed by IT managers and businesses that implement a conscious/systematized Machine Learning strategy are the following:

  • better data analysis and obtaining relevant insights;
  • analysis and perception of risks and opportunities faster;
  • decision-making with insights detected in a timely manner;
  • improvement of internal efficiency (productivity and employee satisfaction);
  • better understanding of customers, business partners, and competitors.

As you have seen throughout this text, Machine Learning is already a reality in the corporate world. It is a concept that can enhance the analytical power of organizations, in addition to providing more intelligent layers of process automation, favoring business competitiveness.

If your company already has a Business Intelligence solution, you can combine this set of technologies with Analytics methods to get even better insights and boost results in areas such as finance, production, marketing and sales, etc.

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