The use of algorithms that can improve themselves based on data and patterns is what we mean when we talk about machine learning. This idea of machine learning originated around the year 1959 with a suggestion made by Alan Turing in his work titled “computing equipment and intelligence.” In this publication, the question “can machines think?” is substituted with the query “can machines accomplish what humans (as thinking creatures) can do? How do we take in knowledge? We can imagine a child and the process of how he solves a puzzle. During this process, the child will take the pieces and try to match some of them 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 such as animals, clouds, forms, etc.. In this scenario, we can imagine that the child is able to solve the puzzle. The child will get better at the procedure as a result of experience if they continue to practice it. We may argue that learning is the result of experience as well as self-development since the capacity to detect patterns is a key component of learning, and the improvement that comes with experience is also a significant component. areas that are now included in machine learning today Machine learning is practically everywhere; it’s on mobile assistants with awesome implementations like “empathy” (the ability to understand tones, personalities, and emotional states); it’s in chatbots, ocr (optical character recognition) with a lot of implementations; and it’s used for data mining. Machine learning is currently expanding and is being improved by the daily use of the technology. Because of this, having knowledge of the idea and the technologies that are associated to it is of the utmost importance. For instance, if in your company you need to develop a smart bot in order to automate the customer care on the most fundamental level, you may install watson assistant in order to have a powerful chatbot. We have experience implementing the most popular machine learning services providers, such as Google Cloud Services, IBM Watson, and Amazon Web Services, in a variety of project implementations and different technologies, such as chatbots, optical character recognition, and data analysis. This puts us at the forefront of this technological progress.
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