The central pillar of machine learning is data. The more information is inserted into the applications, the better their performance will be.
For this reason, this technology reaches its best potential when integrated with resources such as big data, which collects and processes an immeasurable amount of information.
Another essential foundation of machine learning is algorithms. They are a sequence of actions or precise commands that direct the robots to deal with the received data. They are used for countless functions and in organizations of all sectors. See some usage examples:
- cybersecurity: fights fraud in payment systems, preventing cyber criminals’ actions;
- recommendation systems: suggestion of products and offers based on consumers’ purchase history;
- marketing and sales: personalization of marketing actions and improvements in the shopping experience based on in-depth knowledge of potential and current customers;
- predictive maintenance: analysis of data from internet of things (IoT) sensors integrated into machines and equipment;
- Financial services: insights into investment opportunities and other financial services that can be offered to customers of banking institutions;
- Customer service bots: through machine learning, virtual assistants become more competent as they interact with the public;
- logistics: configuration of route maps;
- Health: more accurate diagnoses and disease detection.
There are numerous machine learning applications in companies’ routines, which benefit them in several ways.
What Types Of Machine Learning?
In the universe of machine learning, there are different methods and categories of algorithms used according to each organization’s demands. They are divided by the types that you will see below.
In supervised learning, systems are given a previous data set containing the correct answers. This means previously defined problems and solutions, with the application’s only task being to point out the right results from the variables.
For example, a simple use of this type of machine learning is Google image search, where the algorithm is trained to find those that have been searched for and others like it. The software does not need much training data in this case, as it is created to solve specific situations.
In this model, the opposite happens; there is no expected result, leaving the system responsible for labeling and classifying the data in real-time without human intervention.
This model has less to do with automating decisions and predictions and more with identifying patterns and relationships between data, which human beings would certainly not be able to capture.
Detecting spam by email platforms is one situation that falls into this type. It would be unfeasible, for example, to hire employees to analyze large volumes of emails, discover the standard features that mean spam, and continuously optimize this process.
As the name suggests, the semi-supervised type combines both supervised and unsupervised.
This is what differs most from the other types. In this case, there is no previous set of data. To clarify, it’s like a bot has the autonomy to explore environments, collect data and adapt. The model is used in games, robotics, and financial programs.
What Are The Advantages Of Adopting Machine Learning In My Company?
After reading this, you probably wonder how your business can benefit from machine learning. Thinking about it, we list the main advantages. Check it out next!
Unlimited Data Entry
Machine learning will process an infinite capacity of data from the most diverse sources. This way, it is possible to constantly review them and adjust situations, such as customer relationships, based on their purchasing behavior.
One of the most strategic processes within any company is public segmentation to personalize the shopping experience and generate loyalty. Through machine learning, behavioral patterns are detected by algorithms, organizations gain efficiency in their relationship with their customers, and there are better results in actions in areas such as marketing.
When a business starts to rely on technological resources such as machine learning, its productivity increases because the resource replaces and automates several internal processes that, until recently, were only performed by human beings. Some examples are the faster identification of problems and failures, analysis of reports, and concrete support for decision-making.
As companies gain in productivity with machine learning, they reduce costs. There are several reasons for this statement, the main one being the minimization of the need to hire labor to carry out various tasks developed by the systems.
Personalization Of Communication
Imagine your company communicating increasingly personalized with the public, generating satisfaction, positive perceptions, sales, and profitability. This is possible with the help of machine learning, which brings important insights into each customer’s buying journey, history, and future steps.
As with any new process or resource an organization adopts, implementing machine learning brings challenges, such as adapting the organizational culture, engaging the team, deploying the appropriate technological tools, and choosing the right partners.
However, it is essential to solve all the obstacles that arise since using resources such as machine learning brings intelligence and market differential, promoting prominence about the competition and better results.