master machine learning
Experts use Machine Learning as a supervised learning that will help the machine to identify the process automatically. The main objective of this system is the creation of a model that solves a certain task. In that way, the machine is trained, as Deep Learning, by using large amounts of data.
However, Deep Learning learns from diverse situations and is able to adapt itself and Machine Learning focus in one more specified duty. One of the examples of Machine Learning is the categorization of spam in your email. The system will recognize that certain emails are not necessary for the user and will send it directly away. It learns from data already recompiled and acts consequently to it. The machine never requests you to specify which emails are important or which ones are annoying.
Machines and devices have developed the capability to learn and develop behaviours similar to the human cognitive ability of reasoning. It is thanks to the mathematical algorithms and databases of artificial intelligence. With that improvement, we find ourselves deeply involved with our smartphones and computers’ intelligent assistant. All this technology has a intelligence and that is thanks to machine learning.
The aspects of machine learning that are improving in popularity are image recognition, data mining, predictive analytics and statistics. This association is what makes professionals differentiate machine learning from artificial intelligence. Although machine learning uses AI features such as automated reasoning or natural language processing, it does not need any other features.
However, in the master in machine learning we can learn that nowadays, this discussion about the differences isn’t over. Among both concepts there are similar uses and applications such as bots, intelligence assistants, etc. ML is based on the fact that we can provide data to a machine and it will adapt its algorithm.
Through deep learning, the machine can do that without the necessity of a human being behind its acts. It can also take actions as AI, but those actions are based in past data that has already been provided. Nevertheless, in order to train those machines, we need the intervention of humans who will use data over and over to do it.
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