Surely you are familiar with the concept of ‘Machine Learning‘. This technology is totally linked to Artificial Intelligence and is currently very fashionable. However, not everyone is aware of the advantages of integrating Machine Learning in the company.
If you want to know how it can help you in your professional routine, read on!
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The term Machine Learning refers to automatic machine learning. Tools with this feature develop solutions to evolve and learn from the data they work with. In this way, many processes are automated without even needing human intervention. Basically, this solution automates the generation of analytical models for the study of data.
Advantages of applying Machine Learning in the company
If you want to be one of those professionals who work with Machine Learning in the company, you have to know the advantages of this technology:
- Better customer service. Machine Learning makes it possible to analyze customer preferences in order to offer personalized products automatically. In this way, their perception of the company improves and loyalty is enhanced.
- Reduction of errors. The automatic learning of the management systems applied in the organization helps to ensure that the mistakes made are not repeated. The longer it has been integrated into the system, the more robust it will be.
- Preventive actions. In relation to the previous point, machine learning tools allow us to prevent errors. AI itself discards the most risky actions and those that could jeopardize the development of our product or service.
- Cybersecurity. This technology allows machines to keep up to date with cyberattacks. Considering that most malware uses similar code, machine learning can easily prevent attacks from recurring.
- Fraud detection. Artificial Intelligence can easily detect which transactions are legitimate and which are not if we assign a pattern to these monetary movements.
- Process automation. The automation of routines or mechanical tasks that do not add value is a recurring element in the lists of benefits related to Artificial Intelligence. Thanks to Machine Learning, the machine will know which processes to handle and, over time, will refine them and even expand the number of tasks to be performed.
Americans have been applying Machine Learning in the enterprise for the longest time. According to MIT, the 50% of U.S. organizations uses this technology to facilitate data analysis and obtain more and better insights. 46% is interested in the competitive advantages of Machine Learning, 45% says it wants to increase the speed of data collection thanks to this tool and 44% wants to boost R&D to offer innovative products and services.
Types of Machine Learning
There are three types of Machine Learning depending on the level of supervision they require:
- Supervised Learning. As the name implies, these are Artificial Intelligences that need some human control. In these cases, the Data Scientist establishes what type of data must be related to certain specific elements so that the machine can do the rest of the work. The professional must be in charge of introducing the inputs and outputs so that the technology can find patterns in the information.
- Unsupervised Learning. In these cases, the data is not pre-labeled and the AI has much more autonomy. It is the machine that must find the relationship and structure of the information. A higher density of information is obtained, but the sample is much larger, so the Data Scientist will be in charge of filtering it later on.
- Reinforcement Learning. This system is very different from the two previous ones. Basically, it works with a “reward” system. When the machine gets its operations right, it is given a positive stimulus and if it fails, it is given a negative one. Thus, by trial and error, the machine generates patterns and learns by itself what is the best way to proceed according to the needs of the organization.
After having seen the advantages of this tool, we can say that the investment involved in having Machine Learning in the enterprise can be extremely profitable. However, the capabilities and budget of the organization must be taken into account, as this is an expensive and complex technology.