Thanks to technological advances, concepts like machine learning no longer seem obscure or meaningless. This particular concept revolves around the ability of software to learn by adapting certain algorithms, usually by entering data into the system.
What is machine learning exactly?
Machine Learning is a data analysis technique that teaches computers to do something that comes naturally to humans, that is, to learn from experience. This type of machine learning uses calculation methods to “learn” information directly from the data without relying on a predetermined equation as a model.
How does it work?
The main objective of every learner is to develop the ability to generalize and associate. When we extrapolate this to machines or computers, it means that they should be able to perform with precision and accuracy, familiar tasks, as well as new or unforeseen activities.
And how is this possible? Making them replicate the cognitive faculties of the human being, forming models that "generalize" the information presented to them in order to make predictions. And the key issue here is the data.
In fact, the origin and format of the data are not as relevant, since machine learning is capable of assimilating a wide range of data, otherwise known as big data. In machine learning, big data is not perceived as data but as an enormous list of practical examples.
Most common Machine Learning Algorithms
We could say that their algorithms are mainly divided into three categories: supervised learning, unsupervised learning, and reinforcement learning. Next, we will look at the details that differentiate them.
- Supervised learning: The machine is trained by providing a certain amount of data which is specified and correctly labeled. When the quantum of information is sufficient, new data is introduced without the need for labels, following specific patterns. In other words, the data are problems that we have already solved, which will continue to arise in the future. The idea is that computers learn from a multitude of examples, and from there they can do the rest of the necessary calculations so that we don't have to re-enter any information.
- Unsupervised learning: In this case, no labels are used. Instead, it’s provided with a huge amount of data with the characteristics of an object so that it can determine what it is, directly from the collected information. It is a method very similar to how humans process information.
- Reinforcement learning: The basis of this type is the experience. We can say that the machine is able to learn based on trial and error in different situations. Although it knows the results from the beginning, it doesn't know which is the best decision in a given situation. So, the algorithm progressively associates the patterns of success, repeating them over and over until they are perfected and become infallible.
Areas in which Machine Learning is applicable
Machine Learning is a key to solving problems in the following areas:
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Finance: Banks and other businesses in the financial industry are using Machine Learning to identify credit rating, investment opportunities, and algorithmic trading.
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Healthcare: Based on the symptoms displayed in the patient database, the machine is able to predict if the patient is likely to suffer from a specific disease. Also, thanks to IoT Machine Learning is a fast-growing trend in the healthcare industry. With the advent of wearable devices and sensors, medical teams can use data to assess patients’ health in real-time.
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Automotive, aerospace and manufacturing: Maintenance is one of the core activities of these industries. Predictive maintenance powered by Machine Learning is a driving force for them.
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Transportation: By analyzing data, public transportation companies, delivery companies, and other related organizations can identify patterns and trends which enable them to make routes more efficient, optimize the mobility network, and predict potential problems. All of that enables them to be more efficient, and increase profitability.
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Natural language processing (NLP): From sound waves synthesized by the microphone (be that a computer, a smartphone, or a car microphone) Machine Learning algorithms are able to clear noise, recognize the silence hiatuses between words and essentially understand your language, which enables them to interpret your orders
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Image processing and artificial vision: We know that the images are sets of continuous pixels where each contains information about the color that has to be "illuminated" (for example RGB). Common examples are OCR Optical Character Recognition, which means finding letters, grouping them, finding spaces and being able to decipher texts and the other examples like face recognition, motion detection, etc.
Why do we relate it to artificial intelligence and deep learning?
Machine learning has a very close relationship with artificial intelligence and deep learning. In fact, we can use the example of a matryoshka doll for the mere fact that machine learning is a branch of artificial intelligence.
The latter is characterized by programming the machines to think and learn. Deep learning would be within machine learning since it seeks to emulate the functioning of the neural networks of human beings. If we look at it from this perspective, the machines basically learn to be instructed.
In short, thanks to machine learning, many companies made progress in terms of performance growth, achieving greater efficiency in work routines.
Benefits of Machine Learning
Predict trends
Through the analysis of buying habits, it allows predicting which products will be in demand and when it is a good time to raise or lower prices.
Drive innovation
Thanks to the massive data that machines are able to analyze, machine learning favors the search for new solutions.
Reduce costs
The ML automates tasks in a way that saves human resources. For example, it allows online stores and shopping centers to be optimized according to browsing and customer flow data.
Improve customer relationship
The chatbots respond to customers 24 hours, seven days a week and collect data to deepen consumer knowledge.
Improve ad targeting
ML algorithms can predict which contents are more effective for each target group. They also help improve other parameters like time frames (when the ads go live) and range.
Improve target audience segmentation
Unsupervised learning algorithms discover patterns that allow companies to collect consumer data in a more structured manner.
The future of machine learning for businesses
Although we have already seen what AI is capable of when it comes to our day-to-day activities, how could it benefit the business world?
Numerous comments of digital consumers are gathered on a daily basis and their collection generates vast amounts of data, enabling machines to gain new insight and knowledge and detect trends faster than any human.
While it is true that all that data will make things a lot easier and a lot more efficient, it will still require a lot of effort on behalf of the human factor, as computers don’t have a great command of the language and are generally unable to apply reason to all situations. To put it differently, computers aren’t exactly skilled when it comes to determining the right context.
This means that for machine learning to develop in these areas, experts in each field will have to take the time to train the machines and gradually incorporate them into each of the processes they wish to refine.
Finally, as with all technologies, businesses will have to start understanding the basic principles of this technology, in order to use it in their favor and improve the productivity of all their business operations. For now, it is estimated that this - like many other derivatives of AI - will completely transform the world as we know it.
