Machine Learning - What is it and what brings us into the future

30. Juni 2023

Machine Learning is part of an area of study within the field of Artificial Intelligence that focuses on the use of data and algorithms, whose goal is to be able to reproduce the way human beings learn, improving their method of problem solving.

In the genesis of this technology, theories related to the possible potential of computers and their ability to recognize patterns in data, and how they could learn from them, were analysed, and tested.

Currently, although it is still a recent technology, after a period of research and development that has already elapsed, Machine Learning is much more capable of being a real asset in the entire technological ecosystem and, from a macro point of view, in our entire daily routine. Nowadays, it is possible that the volume of data is greater and obtaining results is faster and more efficient.

What exactly is Machine Learning?

Machine Learning is usually described as a topic of study within the area of Artificial Intelligence. It is dedicated to the use of data and algorithms in order to resemble human behaviour and the way they learn. It is from here that it will gradually optimise the set of skills that it will acquire. The methods used by Machine Learning allow computers and other machines to work autonomously without the need for any kind of human intervention or explicit prior programming.

Essentially, Machine Learning works from the input of data, through which computers can learn, develop, and adapt actively.

Thus, this technology extracts from a large volume of data, essential information that allows the machine in question to look for algorithms that help it to identify patterns, with which it learns in an interactive way.

The dynamics behind Machine Learning are extremely important when it comes to the field of computational sciences.

Through the use of statistical methods, algorithms are taught to perform classifications or predictions, to find key principles in looking for data in projects that require them. From a business point of view, based on this data, it is possible for people to make more informed and thoughtful business decisions through the concrete information collected, thus positively impacting the main metrics necessary for growth.

How does Machine Learning work?

Machine Learning is a field of Artificial Intelligence that teaches computers to learn through the experience they acquire by collecting substantial amounts of data. The algorithms used in this technology are based on computational methods to filter the necessary information directly from the raw data. The adaptabilities of the algorithms in question improve their performance capacity as the number of learning examples increases.

This entire process starts with introducing training data into the algorithm, whether these are known or unknown. To determine the validity of an assay data is entered to assess the accuracy of responses, where it is validated whether predictions and results are compatible or not. If agreement is not found, the teaching process is repeated, until eventually the machine produces the right and expected results. With the continuous learning process, the machine delivers increasingly accurate answers.

Throughout this work, human intervention is limited to the optimization of existing parameters, so that, as a result, there is an increase in better and more accurate results.

This sequence of events allows companies to transform not only the way they perform their daily tasks, but also how they project scenarios likely to be achieved in the future, through predictive analysis. That is, a data analysis technique that uses statistical models, algorithms and that allows predictions or estimates about future events, through the verification of historical patterns and relationships in data sets.

In practical terms, this type of analysis can be used for various purposes, such as: identifying potential risks, anticipating demand peaks, optimising processes, making strategic decisions, customising recommendations, or offering insights for decision-making.

Existing Types of Machine Learning

In Machine Learning, we can separate the types of algorithms according to their learning dynamics. Consequently, there are three specific types of learning:

Supervised learning

This type of learning aims to teach the computer to predict a valid result for a new set of introduced data. This form of learning confers, simultaneously, the results as well as the data that preceded it, which in turn allows the computer to be able to adjust its parameters until reaching a prominent level of accuracy.

It is through this type of learning that data is introduced and removed from the computer algorithm, processing them between input and output. This allows the algorithm to alter and optimise the model in order to be able to create a data output as closely aligned with the expected results.

As the name implies, supervised learning introduces the necessary information into algorithms so that the computer can learn through examples. One part of the information given to it is seen as output data, the other part is used as input.

This learning is quite efficient and is starting to gain a lot of recurrence in a wide variety of businesses, where it could be an asset in functions such as: sales forecasts, inventory optimization, fraud detection, etc.

Unsupervised learning

Unlike the previous one, and with also different applications, in this type of Machine Learning, learning is normally used when there is a large volume of data to process, whether structured or unstructured. In this case, they are entered without any type of identification or instruction on how they should be organised. The aim is to find patterns and groups that are not obvious to the human eye. Contrary to the supervised approach, this type of learning searches, among the data, for less obvious patterns that are imperceptible to a human and highly manual analysis, which, in practice, could be a catalyst for the generation of valuable information through insights clear for decision-making in organisations.

It is through this type of learning that models capable of establishing predictions are developed. A common application of this form of learning is clustering. Clustering, a process in which a model is created that groups objects, considering their specific properties, as well as their various associations.

It is also in unsupervised learning that a methodology for identifying common rules between each set is used. This capability could be a valuable tool to turn raw data into more refined, structured information, allowing it to work with grouped information segments.

Reinforced learning

This is the learning method most like the way humans receive, process, and retain information. The algorithm learns to interact with the environment that surrounds it, receiving positive or negative reinforcement.

In one example, we have the algorithm learn a board game, such as chess. In this scenario, the machine's objective, as in real life with humans, is to take the best actions on each move to maximise its chances of winning. Thus, the algorithm interacts with the environment - the chessboard, and receives rewards based on the actions it takes, be it victory, defeat, or draw. Initially, the algorithm performs random moves, but as it receives feedback, it learns to take the best actions to increase the odds of winning. As the game progresses, the algorithm improves its strategy using reinforcement learning. It is through this process of learning and exploration that the machine becomes more efficient in the game, learning to perform the best actions in a complex environment.

This type of machine learning does not require such incisive supervision and is therefore more accessible for managing unstructured data.

What are the benefits of Machine Learning

A deep understanding of the real applications and advantages of this innovative technology allows for a greater understanding of the sectors and subjects where Machine Learning can be an essential tool.

Aid in decision-making

Machine Learning allows you to extract valuable insights and make data-driven decisions. Learning algorithms can analyse large volumes of data, identify patterns, trends and correlations that may not be obvious to people.

Task automation

With Machine Learning it is possible to automate repetitive tasks of the daily routine, freeing professionals for more complex tasks in which creativity or another characteristic distinct from humans makes the difference.

Customization and recommendations

Machine Learning algorithms can analyse user behaviour and preferences to offer personalised recommendations. In our daily lives we can find this technology to be part of our routine through the services of streaming music, video, and online shopping, where systems recommend content based on individual interests.

Fraud detection and security

This technology is effective in detecting unusual patterns and identifying actions that may be associated with fraudulent behaviour in real time. This characteristic could be decisive in sectors such as finance, where, currently, one of the central themes of innovation and development involves preventing fraud and detecting suspicious activity on the network.

Forecasts and analysis

Through Machine Learning algorithms it is possible to perform advanced analysis based on historical data. In practice, through this technology it is possible to establish forecasts and establish a market analysis in order to anticipate potential scenarios, enabling companies to adjust their strategy accordingly.

Continuous improvement of processes

Continuously, the new data that is generated with the business serves to learn the algorithm in an uninterrupted way. This capability allows Machine Learning models to be constantly adapting and optimising, providing more accurate and efficient results over time.

What applications does it have?

Health

In the health area it can be used in the analysis of medical data, diagnosis of diseases, identification of patterns in exams, analysis of medical images, drug discovery, research, and development in the area of ​​genetics.

Finances

In the financial sector, as we have already mentioned, Machine Learning is essential for detecting transaction fraud, but also for credit risk analysis, market projections, market sentiment analysis, portfolio management and optimization of investment portfolios.

Retail

In retail, more specifically in online commerce, Machine Learning gains a leading role in the continuous improvement of consumers' experience. Through product recommendations, demand forecasting and more detailed segmentation, electronic purchases gain a more personalised dynamic and adapted to each public.

Marketing and publicity

In this area, this technology is crucial for target audience segmentation, campaign personalisation, content optimisation, sentiment analysis on social networks and, in general, for the optimization of promotion and distribution channels.

Industry

In industry, Machine Learning plays a particularly significant role in the preventive maintenance of equipment, optimization of supply chains, demand forecasts, quality control and process automation.

Transport and logistics

In logistics it is possible to optimise routes, anticipate delays, manage fleets, track goods, data analysis - through sensors in vehicles, and timely detection of anomalies in the transport system that could compromise the normal functioning of the entire chain carriage.

Artificial Intelligence and Machine Learning - what are the differences?

If, on the one hand, Artificial Intelligence refers to the broader field of computer science, which focuses on creating intelligent systems, on the other hand, Machine Learning is a sub-area of ​​Artificial Intelligence that focuses on the development of algorithms and models capable of learning from data. Thus, while Artificial Intelligence covers a broader field, Machine Learning is a specific approach within AI, which uses algorithms and statistical models to train systems from data.

Machine Learning and Data Science - What is the relationship?

While Machine Learning focuses on developing algorithms and models that can learn from data and make decisions or make predictions, Data Science is a broader field that involves collecting, preparing, analysing, and interpreting data to extract meaningful information and act based on informed decisions.

Data Science professionals use techniques and tools, including Machine Learning, to explore and understand data, develop predictive models, and create solutions to complex problems. In short, the main difference between Machine Learning and Data Science is that the former is a technique that focuses on developing algorithms capable of learning from data, and the latter, it is possible to gain insights and make informed decisions.

How you can learn Machine Learning

Learning Machine Learning offers a broad horizon in oportunidades de carreira and professional growth. This is an area where decision-making is informed and automation and efficiency are privileged, driving technological innovation to solve complex problems.

If this is an area where you want to develop skills, these steps can help you on your professional journey:

• - Study the fundamentals of programming
• - The bases of mathematics and statistics will be an added value
• - Learn the basic concepts of Machine Learning (types of learning, algorithms, clustering, etc.)
• - Acquire more knowledge both in online tutorials and in books
• - Apply your knowledge to new projects
• - Familiarise yourself with IT frameworks and existing libraries - you have tons of options
• - Join communities, learn and share

What is the future of Machine Learning?

Machine Learning is currently playing a crucial role in many sectors. Thanks to its ability to access difficult-to-resolve answers and the fact that it can establish future predictions through predictive analysis, it increasingly stands out as a useful tool for the evolution of society.

Technology has progressed in a more sophisticated and at an accelerated pace. The future of Machine Learning can go through continuous advancement in areas such as Deep Learning, where more complex models can be developed. There will be an improvement in processes, with increasingly daily routine tasks being automated more efficiently.