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Machine Learning — what is it?

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Machine Learning is a method of data analysis that involves the automatic creation of analytical models.

Machine Learning assumes that algorithms can learn independently.

Learning should be understood primarily as the identification of patterns and making decisions with limited human involvement.

Although the above definitions can seem to a certain degree uncommunicative, after reading this article, everything that is abstract in them will gain a much more concrete character.

What is Machine Learning? Where can Machine Learning be used?

Why is Machine Learning important? What is the business justification for using Machine Learning algorithms?

You will find answers to these questions and more in this article. As always, we cordially invite you to read on!

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Machine Learning — what is it?

Questions about what Machine Learning is and how Machine Learning works can be answered by explaining that Machine Learning is a part of Artificial Intelligence (AI).

Machine Learning is aimed at sharing data with systems, thanks to which they can learn and improve automatically and independently.

First things first!

First, let us organize a little the terminology we'll use in this article. Machine Learning vs. Artificial Intelligence — that's our first issue.

An image illustrating artificial intelligence, machine learning, deep learning

Machine Learning is not the same as Artificial Intelligence.

These terms are not synonyms and can't be used interchangeably, although sometimes, it can be done in the literature on this subject.

To be consistent, the relationship between these concepts looks as follows:

  • Artificial Intelligence (AI) — it's the broadest term, encompassing concepts of narrower scope.
  • Machine Learning (ML) — a narrower term contained in AI, but it's not reduced to it.
  • Deep Learning (DL) — the term with the narrowest scope that is a part of Machine Learning and Artificial Intelligence.

Do these terms, fields of knowledge, technologies differ?

Artificial Intelligence, in the most general sense, is a science about mimicking the human ability to learn, human methods of learning and information processing, cognition, categorizing of data, and problem-solving.

As you can read in the article "Artificial intelligence (AI) vs machine learning (ML)," thanks to AI, a computer system uses math and logic to simulate a way of reasoning which humans use for learning.

It's about the continuous addition of new information and making, thanks to constant learning, better, more accurate, beneficial, and effective decisions.

Machine Learning is a specific subset of Artificial Intelligence. Its goal is to create algorithms to teach a machine how to learn.

In other words, Machine Learning is a specific use of Artificial Intelligence.

It's the process of creating, using mathematical data models to make algorithms independent of the need for direct human instructions.

So, Machine Learning aims to learn as independently as possible and strive for improvement based on previously achieved experience.

The goal of specialists developing AI technologies is to create algorithms that make it possible to perform tasks in a way that is as close to the human way of thinking as possible, and intelligence is a crucial part of it.

The idea behind Machine Learning is the conviction that machines should have access to data and allow them to learn independently.

Let's conclude the above with a sentence from the article "How machine learning works," published on the Algorithmia.com blog.

And it reads as follows: "Almost any task that can be completed with a data-defined pattern or set of rules can be automated with machine learning."

We need to admit that this sounds promising, especially from the business perspective. But also from the perspective of the needs of users of digital products.

In practice, this means the ability to automate many processes which until now needed to be performed by appropriately educated, prepared, and often tired, uncommitted, distracted people.

Although the psychological aspect may not seem as important, it's worth mentioning because solutions based on Machine Learning are free of typical human ailments and imperfections.

Because, in many cases, contexts, processes, goals, and needs have considerable significance.

They affect the efficiency, cost-effectiveness, and competitiveness of an organization and the level of satisfaction of its customers and users.

The process of machine learning

For example, imagine a simple customer assistant on a website of a delivery company who, 24/7 and 365 days a year, supports any number of customers, answers their questions (very typical and repetitive ones), and never gets tired, frustrated, or feels a lack of meaning.

Algorithms used in Machine Learning are already capable of meeting many typical needs of customers and, as in the above example, answer a lot of questions.

What methods are used in Machine Learning?

Machine Learning uses various methods, but four of them are the most popular. And these are:

  • Supervised Machine Learning
  • Unsupervised Machine Learning
  • Semi-Supervised Learning
  • Reinforcement Learning.

How do they differ? Let's refer to definitions presented in the article "What is machine learning?" published on IBM's blog, which significantly contributes to the development of Machine learning.

Supervised Machine Learning involves using labeled datasets to train algorithms that classify data or predict results.

As input data enters a model, it adapts its weights until it fits appropriately.

This happens as a part of the cross-validation process, which helps avoid overfitting or underfitting the model.

In other words, in Supervised Machine Learning, data are labeled to tell a machine precisely what patterns it should look for.

Unsupervised Machine Learning uses Machine Learning algorithms to analyze and group unlabeled datasets.

These algorithms discover hidden patterns or groups of data without the need for human intervention.

Unsupervised Machine Learning is used to find similarities and differences in information, thanks to which it's a perfect solution for analyzing exploratory data, cross-selling strategies, customer segmentation, and recognizing images and patterns.

Unsupervised Machine Learning algorithms are used when data for learning is not classified or labeled.

Unsupervised Learning studies how systems can infer a function describing a hidden structure from unlabeled data.

The system doesn't determine input data but explores them and can draw conclusions from datasets to describe hidden structures from unlabeled data.

In other words, in Unsupervised Learning, the algorithm tries to learn a certain inherent data structure through only unlabeled examples.

The data don't have labels, and the machine's task is to find all patterns that can be found.

Semi-unsupervised Learning tries to combine the advantages of the abovementioned methods — Supervised Learning and Unsupervised Learning.

During the course of learning, the algorithm uses a smaller labeled dataset to direct classification and feature extraction from a larger unlabeled dataset.

Semi-supervised Learning involves training a model with a minimal amount of labeled data and a huge amount of unlabeled data.

In Reinforcement Learning, the reinforcement algorithm learns by trial and error to achieve the desired goal (learning by reinforcing).

Metaphorically speaking, the algorithm tries many different things and is awarded or punished depending on whether its behavior helps or hinders the achievement of the established goal.

Usually, Semi-supervised Machine Learning is selected when the acquired labeled data require qualified and appropriate resources to train them and/or learn based on them.

It's worth noting and remembering that the effectiveness of Machine Learning depends on the number of data and computing power of the infrastructure used for this purpose.

Despite these limitations, Machine Learning ensures outcomes that are:

  • Relatively accurate
  • Relatively quickly acquired.

Where is Machine Learning used?

Before we point out fields, industries, sectors, and problems in which and for which Machine Learning is most frequently used, by way of introduction, we'll say that it's a concept, technology whose origins date back to 1959.

Back then, the first definition of artificial intelligence was created by Arthur Samuel.

Of course, today's Machine Learning is more advanced than in its beginnings.

Modern systems learn based on tremendous amounts of diverse data, and the iterative aspect of this science allows them to continuously improve the quality, accuracy, and credibility of results.

The effectiveness and increasing perfection of Machine Learning mean that it's being used in the following:

  • Internet browsers
  • E-mail clients
  • Recommender systems (e.g., in E-Commerce and recommendations engines of VoD platforms)
  • Applications conducting predictions and investment recommendations
  • Applications estimating financial, credit risk (e.g., credit scoring)
  • Applications detecting, and recognizing faces, objects, or movement
  • Applications recognizing voice (e.g., voice assistants)
  • Applications for translation
  • Cars.

Generally speaking, thanks to the use of sophisticated statistical methods, Machine Learning algorithms are used to:

  • Diagnose
  • Recognize
  • Predict
  • Discover regularities, patterns, rules
  • Classify
  • Identify
  • Detect anomalies.

When it comes to industries, Machine Learning is most often used in the following:

  • Administration
  • Bioinformatics
  • E-Commerce
  • Pharmaceutics
  • Marketing (e.g., political marketing)
  • Medicine and public health
  • Healthcare
  • Production based on fossil fuels
  • Transport
  • Financial services.

The scope of use of Machine Learning continuously expands. Hence the above list will need to be updated with time.

We can confidently say that Machine Learning has a promising future ahead of it, as demonstrated by the continued investment by major players in this technology.

According to Motley Fool, which writes in the article "Investing in Machine Learning Stocks," global spending, investments in Machine Learning by 2025 are supposed to reach 100 billion dollars resulting in an annual return of 40%.

Implementing Machine Learning solutions and using Machine Learning models is slowly becoming a necessity and a strong trend that creates market pressure. Already many businesses are based on Machine Learning.

Thus, it's worth keeping in mind sentences from the article "Why You Should Invest In Machine Learning," in which we can read that organizations today are almost forced to follow this technological trend.

Being even one step ahead is essential to offer a better user experience for our customers.

Machine Learning also helps use resources and offer services more effectively.

It's also worth remembering that Machine Learning enables organizations to:

  • Create models faster, more precisely, and fully automatically
  • Analyze more data with a more complex structure
  • Obtain more accurate results that are more useful
  • Identify opportunities, potentials, and threats
  • Automate routine tasks, thanks to which it's possible to increase savings and efficiency
  • Make more rational, accurate, and profitable decisions.

Is there the best programming language for Machine Learning?

While writing about Machine Learning algorithms that support the development of businesses, it's impossible not to breach the subject of an adequate programming language recommended for creating Machine Learning applications.

In the article "7 Top Machine Learning Programming Languages," published on Code Academy's blog, Python is one of the most important programming languages recommended by the authors of the text for creating Machine Learning apps.

The other six are:

  • R
  • C++
  • Java
  • JavaScript
  • Go.

Why is Python the first choice for such tasks?

Its advantages include the following:

  • Simple syntax
  • Clarity
  • Flexibility
  • Availability of numerous libraries and frameworks (e.g., OpenCV, Tensor Flow, PyTorch, NumPy, SciPy).

Furthermore, Python significantly facilitates deploying and implementing complex concepts such as calculus or linear algebra. But that's not the end.

Another advantage of Python is the ability to flexibly choose between object-oriented programming and scripting. Python also doesn't require compiling; the entered changes are visible immediately.

Python can be easily combined with other programming languages, thanks to which it's possible to create desired functionalities.

We also can't forget about the universality of Python, which can work on any platform (e.g., Windows, MacOS, Linux).

Machine Learning — what is it? Summary

  1. What is the difference between Artificial Intelligence and Machine Learning? Machine Learning for programmers is a narrower term, and it's a part of Artificial Intelligence.
  2. Machine Learning and Artificial Intelligence are not synonyms. AI Machine Learning is not a correct term.
  3. Although Deep Learning and Machine Learning are semantically similar, they're not synonymous.
  4. Artificial Intelligence is a science about mimicking the human ability to learn and information processing, cognition, categorizing of data, and problem-solving.
  5. Machine Learning focuses on creating algorithms and using data that are supposed to teach a machine how to learn.
  6. Machine Learning involves sharing data with algorithms, thanks to which they can learn and improve automatically and independently (Machine Learning models).
  7. The basic premise of Machine Learning is that algorithms can independently learn and do it in a manner similar to the way humans learn.
  8. In Machine Learning, science is understood as the ability to recognize patterns and make decisions with limited human intervention.
  9. During the development of Machine Learning, it was noted that "Almost any task that can be completed with a data-defined pattern or set of rules can be automated with machine learning."
  10. Machine Learning uses 4 methods for learning.
  11. In Supervised Machine Learning, data are labeled to tell a machine precisely what patterns it should look for.
  12. In Unsupervised Learning, the algorithm tries to learn a certain inherent data structure through only unlabeled examples.
  13. In Semi-supervised Learning, during the course of learning, the algorithm uses a smaller labeled dataset to direct classification and feature extraction from a larger unlabeled dataset.
  14. In Reinforcement Learning, the reinforcement algorithm learns by trial and error to achieve the desired goal.
  15. The effectiveness of Machine Learning largely stems from the amount of data and computing power.
  16. Machine Learning is often used in Internet browsers, recommender systems, applications for predictions and investment recommendations, applications estimating credit risk, detecting and recognizing faces, objects, movement, voice, translation apps, and autonomous cars.
  17. Machine Learning algorithms are used to diagnose, recognize, predict, discover regularities, patterns, and rules, and classify, identify, and detect anomalies.
  18. Machine Learning in business (neural networks, automatization of processes) makes it possible to analyze more data with more complex structures, identify opportunities, risks, and potential threats, automate routine tasks and processes, and make more rational, accurate, profitable decisions.
  19. Machine Learning algorithms are increasingly often used to automate processes, support functionalities such as recommendation mechanisms, and create new services, e.g., chatbots that provide detailed information and serve customers in real time.
  20. Python is a programming language recommended for creating Machine Learning applications.
  21. Python is universal, flexible, and clear, with numerous libraries and frameworks that facilitate the creation of Machine Learning applications.
  22. The Machine Learning process, the accuracy of learning, data analysis, exploration of data, neural network, pattern recognition, and Machine Learning automation are terms that are not just abstracts but are increasingly often used in daily life in digital products based on Machine Learning models.
How you like that:
Journal / Redaktor
Author: Radek
UX Writer and researcher by education + experience. Collects The Story's knowledge and shares it on the Journal.
Reviewer: Yaroslav Shatkevich

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