Machine learning is the implementation of algorithms that can improve themselves based on statistics and patterns. This machine learning concept born around 1959 with an Alan Turing's proposal in his paper "Computing Machinery and Intelligence", in which the question "Can machine think?" is replaced with the question "Can machines do what we (as thinking entities) can do? |
How do we learn?
We can imagine a kid and the process of how puzzle solving, in this process the kid will take the pieces disordered and will try to match some before realizing that he needs a strategy to improve the solving task, he can ask his parents for help and they will teach him to look for borders first, group colors and find patterns based on known things like animals, clouds, forms, etc.. With practice the kid will improve the process based on experience. The improvement with experience and the ability to find patterns, those are important learning elements, we can say that learning is a product of experience and self-improvement.
Fields in the machine learning
Today machine learning is almost everywhere, is on mobile assistants with awesome implementations like “Empathy”(the ability to understand tones, personalities and emotional states), in chatbots, OCR(Optical Character Recognition) with a lot of implementations and data mining, machine learning today is growing and is being improved by the daily use. This makes super important the fact to know about the concept and related technologies. For example, if in your business you need to implement a smart bot to automate the customer service at the basic level, you can implement Watson Assistant to have a strong chatbot.
In Proximity we are in the forefront of this technological progress, we have experience implementing the most popular machine learning services providers like Google Cloud Services, IBM Watson and AWS, in different project implementations and different technologies such as chatbots, OCR and Data Analysis.
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