Machine Learning: This is probably the most frequently asked question that people new to the area ask. The doubt is natural, but it can be easily answered and understood if we think about it.
Maybe it’s unnecessary to think; look at a baby. Have you noticed babies’ behavior at the age when they begin to imitate their parents, siblings, and other people? They observe a lot at this stage of their lives and soon start repeating everything they see and like. Learning by observation, repetition, and imitation is one of the most important aspects, especially if the first imitations are wrong, so they can do it correctly.
As we grow and age, the cognitive process changes and becomes richer in terms of factors that interfere with learning. As we acquire more and more knowledge, these contribute to acquiring new ones at an ever-increasing speed. So even though people who learn fast are often said to be smart, intelligence is not just learning.
Learning is related to intelligence, it is part of it, but it is not the same thing. It’s very simple to understand. We are often challenged to solve new questions without prior knowledge and data to base our decisions on, that is, the observation and repetition of data common in the learning process, using only intelligence to evaluate and discern.
So while Machine Learning often integrates Artificial Intelligence and is even fundamental to making it possible, AI is much broader than Machine Learning.
What Is Machine Learning?
After knowing that it is not synonymous with Artificial Intelligence, this must be the question you want to be answered. Thus, a possible formal definition could be: “Machine Learning is the ability that a machine acquires to learn as humans do, autonomously, through the collection of data and information, evaluation of them, to decide finally.” But you’re here for more than that.
Machine learning is the part of artificial intelligence that allows computers to develop behavior similar to human learning. When given sufficient data, these computers can learn, grow, change, and develop in ways independent of human interaction or conventional programming.
The process starts with feeding good quality data and “training” the computer by building learning models from the data and different algorithms. There is variation in the algorithms depending on the type of data we have and the task we are trying to automate.
So, for example, if the goal is to create an anti spam filter, the learning algorithms will not be the same as those used to learn how to drive a vehicle, and the nature of the data, either. And so, we have just given two practical examples of Machine Learning applications.
There are currently many applications in which Machine Learning has been used by companies in our daily lives, such as Netflix movie directions, the best routes on Waze map, Amazon purchase directions, and Google search results. Even when the credit card company calls you to validate a certain transaction, it uses Machine Learning to identify a suspicious transaction or possible fraud.
What Types Of Machine Learning?
Two of the main methods or types of machine learning are supervised learning and unsupervised learning. There are also, to a lesser extent, semi-supervised and reinforcement learning:
In this modality, a large amount of data is provided to the learning algorithm. Among the data, an amount of them is associated with the right answer or the expected output, which are called labels. Due to this type of behavior, this method is also called inductive since there is deliberate interference to obtain the results that are expected or intended;
As can be assumed, contrary to the previous one, the received data does not contain the expected outputs. In this way, the results are related to the patterns found in the data and not to a label, and the volume of data must be large enough for the patterns that lead to the responses to be identified. This model is also called deductive, as it resembles the human mental model in which the answer is obtained by completing the evaluation of the data;
It includes data from both of the above models, and so learning can take place from both supervised and unsupervised data; when we have a large amount of training data but only some are supervised or contain the labels;
Also known as the model or method of rewards, the agent (author of the action) learns the results through actions that he can do (as an agent), acting in the environment and producing different rewards. The biggest rewards are associated with the best decisions. Therefore, reinforcement learning aims to learn the best decision to make when there are multiple alternatives to choose from and different outcomes, all of which may be true.
Machine Learning In The Enterprise Environment
We have already seen situations in which we benefit from this technology in our daily lives, but the number of applications in the area is already immense. Anyone who has not yet joined this wave should seriously consider joining.
Companies need to understand that Machine Learning must be associated with products and services, with several objectives, ranging from making them better and more attractive, as well as cheaper, contributing to aspects of security, performance, usability, and even improving the company’s image and not just because it is a trend or a cutting-edge technology.
The first step towards implementing machine learning in the company to be a tool for business improvement is to objectively stipulate what you want to do better or what demands your customers have and what Machine Learning can meet. Soon, the question will no longer be whether or not you will use it but how much you will use it.
Machine Learning is not a new subject; it is not just a trend or something that will happen in the future. It is already part of our realities and consists of a technology capable of improving the products and services we consume, with clear benefits for the companies that use it and for us.
Also Read: Artificial Intelligence In The Digital World