What is Machine Learning? A Short Guide to Machine Learning

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Machine Learning is something that everyone who has encountered the topic of artificial intelligence has heard of. However, do all of them know exactly what machine learning is? We invite you to familiarize yourself with our short guide to machine learning, which will allow for a better understanding of this concept.

What is Machine Learning? – Definitions and Basics of Machine Learning

Machine Learning, also known as ML, is the foundation of artificial intelligence. It is a group of algorithms that allow computers to learn from available data. This learning process involves finding and recognizing statistical dependencies. As a result, a computer program identifies characteristic features of a specific data group and creates a rule to determine whether new data belongs to that group or not.

Furthermore, machine learning typically means that a computer program improves its accuracy over time as it learns from an increasing amount of data. Importantly, the quality of machine learning depends on the quantity and quality of the available input data. The richer the dataset, the more accurate the results will be.

Basic methods of machine learning

Machine learning refers to four basic methods. These are:

  • Supervised learning,
  • Unsupervised learning,
  • Reinforcement learning,
  • Deep learning.

Supervised learning involves providing a sequence of data to an algorithm, consisting of both the features of a given object and the class or assigned numerical value, such as age, of the object. During the learning process, the computer program is tasked with recognizing what attributes classify an object into a particular category. This allows the algorithm to classify subsequent objects that exhibit those same features into the same category. Machine learning programs based on supervised learning assist in tasks such as spam detection, language recognition, or determining credit risk.

In the case of unsupervised learning, we provide sequences of data to algorithms consisting solely of object features. The program’s task is to organize and divide these objects into groups without the need for categorization. Unsupervised learning can be used, for example, in creating personalized newsletters.

In contrast to the previous two methods, reinforcement learning is characterized by the absence of training data. Instead, the program interacts with the environment and learns through trial and error how to best interact with that environment. Examples of this include ChatGPT. Furthermore, reinforcement learning is utilized in various fields such as robotics, autonomous vehicles, and automated stock trading.

Deep learning is a subtype of machine learning. It is based on artificial neural networks consisting of at least three layers responsible for various tasks related to data analysis and performing specific actions on them. Deep learning involves turning off certain connections in the neural network and strengthening others.

How can machine learning support business?

Machine learning has been utilized in business almost from its inception. This is because machine learning enables:

  • Establishing relationships between variables, allowing us to predict future behaviors.
  • Assigning features to specific groups.
  • Considering only relevant variables for further information processing.

As a result, machine learning-based systems enhance operational efficiency. This allows companies to make better use of process automation, thereby saving time, money, and resources.

Moreover, machine learning programs can make decisions quickly and more accurately, reducing the number of errors made by humans.

Is machine learning ethical?

Machine learning, or algorithms in essence, are “ethically neutral.” However, they can be used in ways that have negative consequences for individuals and even entire communities.

One example is the use of machine learning in medicine. Today, algorithms are capable of detecting medical conditions and proposing treatment plans for patients. However, this raises the question of who is responsible for any potential errors in such cases.

The ability to analyze vast amounts of data and draw conclusions from it can be exploited to manipulate public opinion. In this case, machine learning can serve as a tool for winning elections, organizing protests, and ultimately implementing changes that may be detrimental to society.

Machine learning and artificial intelligence are fields that demonstrate the need to develop effective mechanisms to protect individuals and communities from the unethical use of this technology as it continues to evolve.

Machine learning and artificial intelligence are fields that demonstrate the need to develop effective mechanisms to protect individuals and communities from the unethical use of this technology as it continues to evolve.

The most popular solutions related to Machine Learning

Currently, one of the most well-known solutions related to machine learning is ChatGPT, which generates responses to user questions. However, AI programs based on machine learning can be utilized in practically any industry.

As mentioned earlier, computer programs utilizing machine learning can detect pathological changes based on diagnostic images or plan treatments for patients.

In the financial industry, algorithms assist in calculating risks, detecting fraud, and approving loans.

Machine learning also forms the foundation of many internet applications that predict which TV series we might enjoy based on our viewing history.

Furthermore, machine learning is employed in traffic management, demand forecasting, and quality control. In other words, wherever there is a need for quick analysis of a large amount of data, one can count on artificial intelligence based on machine learning to handle it.

Machine Learning – Summary and Sources

You now understand what machine learning is, its types, and how it is applied in practice.

Do you have any ideas on how you can utilize such computer programs in your industry?

Talk to us.

Source: https://www.iab.org.pl/wp-content/uploads/2023/03/SCMP_Artifficial-Intelligence_raport_2022.pdf