Machine Learning: What It Is and How It Works


Machine Learning

Machine Learning is the science of making computers act without being explicitly programmed , in other words it is teaching computers to do what is natural to humans and animals, which is to learn from experience .

Over the past decade, machine learning has given us self-driving cars, hands-on speech recognition, effective web search, and a vastly improved understanding of the human genome.

Today, machine learning is so pervasive that you probably use it dozens of times a day without knowing it.

In this article we will talk about what Machine Learning is, its Applications , Methods , what makes it different from Deep Learning and much more.

Machine Learning 2



Machine learning is an application of Artificial Intelligence that gives systems the ability to automatically learn and improve with experience , without being explicitly programmed. Machine Learning focuses on developing computer programs that can access data and use it and learn for themselves .

The learning process starts with observation or data, such as examples, direct experience, or instructions, with the aim of looking for patterns in the data and making better decisions in the future , based on the examples provided. The main purpose is to allow computers to learn automatically without human intervention or assistance, and adjust actions accordingly.

This learning is carried out through the use of Algorithms, which are rules that show the step-by-step necessary for the realization of a problem . Through a logical, defined and finite sequence of instructions, they determine the path to follow to perform a task.



You might be wondering what are some examples of machine learning and how does it affect our lives? Let’s look at some examples where we already use the result of machine learning in our daily lives:

  • Satellite navigation applications, such as Waze, use Machine Learning to learn the best paths on the map from the users themselves, in addition to receiving information about traffic jams, accidents, road blocks, adverse weather conditions, etc. and provides the most suitable routes in real time;
  • Facebook uses deep neural networks to decide which ads to show and to which users, through machine learning to find out as much as it can about us and to group us in the most insightful ways to serve us ads;
  • Google trained algorithms for 2 years to analyze the use of electrical energy in its server centers, and with that, it managed to reduce energy consumption by 15%, using Machine Learning to optimize this consumption;
  • Digital Banks, such as Nubank, for example, use Machine Learning to approve or not a customer for the credit card and also to define the credit card limit.



It seems like a recent issue, but this idea of ​​teaching machines has been around for some time.

One of the greatest classics of science fiction literature, “I, Robot”, written by Isaac Asimov, in 1950 , and already dealt with the man/machine relationship, through the Three Laws of Robotics.

Machine Learning ideas and research have been around for decades. However, there has been a lot of action and excitement recently.

The obvious question is why is this happening now, why is there so much talk about it today when machine learning has been around for several decades?

This is basically due to 3 factors that have changed, and that ended up influencing the popularity of the subject:

  • The amount of data generation is increasing significantly with a reduction in cost.
  • The cost of data storage and computing has been reduced significantly.
  • The cloud (data storage on the internet) has allowed a democratization of computing for the masses.

These factors combine to create a world where we’re not just creating more data, but we can store it cheaply and run massive operations on it. This was not possible before, although machine techniques and algorithms were well known.


Both Machine Learning and Deep Learning are pillars that support Artificial Intelligence, however they are not the same thing.

As we have seen, it is the use of algorithms to organize data, recognize patterns and make computers learn from these models and generate intelligent insights without pre-programming.

Deep learning, or deep learning, is the part of machine learning that, through high-level algorithms, mimics the neural network of the human brain .

So Deep Learning is a more advanced and in-depth form of Machine Learning. Where it combines complex algorithms built on several layers of non-linear processing that simulate the way neurons think, with an immense amount of data, making it possible to perform advanced and complex tasks without human interference.

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Machine Learning and Automation are very different things, most of the automation that has taken place in the last few decades has been rule-driven automation . For example, automating flows in our mailbox needs us to define the rules. These rules act the same every time.

On the other hand, it helps machines learn from past data and change their decisions/performance accordingly. Spam detection in our mailboxes is driven by machine learning. So it continues to evolve over time.

The only relationship between the two is that allows for better automation.


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That algorithms are generally classified as supervised or unsupervised.

Supervised learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events .

From the analysis of a known training dataset, the learning algorithm produces an inferred function to make predictions about the output values.

The system is capable of providing targets for any new entry after sufficient training of People Management. The learning algorithm can also compare its output to the correct and intended output and find errors to modify the model accordingly.

On the other hand, unsupervised learning algorithms are used when the information used to train is not classified or labeled .

Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. The system does not discover the correct output, but explores the data and can draw inferences from datasets to describe hidden structures of unlabeled data.

Semi-supervised algorithms are somewhere between supervised and unsupervised learning in that they use both labeled and unlabeled data for training – usually a small amount of labeled data and a large amount of unlabeled data .

Systems that use this method are able to significantly improve learning accuracy. Generally, semi-supervised learning is chosen when the labeled data acquired requires qualified and relevant resources to train/learn from them. Otherwise, acquiring unlabeled data generally requires no additional resources.

Reinforcement learning algorithms are a method of learning that interacts with its environment, producing actions and discovering errors or rewards. Trial and error research and delayed reward are the most relevant features of reinforcement learning.

This method allows machines and software agents to automatically determine optimal behavior within a specific context in order to maximize their performance. Simple reward feedback is needed for the agent to learn which action is best; this is known as a reinforcement signal.

Machine Learning allows the analysis of large amounts of data. While it generally provides faster and more accurate results to identify profitable opportunities or dangerous risks, it may also require additional time and resources to properly train you. Combining machine learning with AI and cognitive technologies can make it even more effective at processing large volumes of information.

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