We referred to Machine Learning a few times in earlier modules. In fact Machine Learning is not only related to AI but many other disciplines and probably is becoming a discipline on its own. If we want to describe machine learning in layman terms, “It is a computerized system which can learn on its own or which can infer from data on its own”. Another definition is “a program which looks at patterns in data and try to derive intelligence out of it”. A more detailed and precise definition is probably not possible to be provided. The examples that we provide will give better idea. Interestingly machine learning is addressing problems which are also of that type; harder to define but easier to provide examples of. For example identify a mail being spam or identify a packet being malicious or find if the image of a person indicates he is either bored or happy. There are many similar cases where the problem is either ill-defined or dynamically changing to define it precisely. Dealing with them we need ML. ML solutions look at many examples provided; for example sad and happy faces, spam and non-spam mails, malicious and non-malicious packets etc. ML looks at the features of the data and lean to discern how it can relate a typical set of features to a typical category.
ML is gaining popularity like never before. Currently there are many attempts to use ML in various fields. Speech recognition (working on learning about who is speaking and what is being spoken), vehicular control (learning traffic, road conditions and help vehicles choose a proper path for a given destination); deep learning (which enable further learning from the images and other inputs), astronomical structure classification, self-driving cars and healthcare are a few domains which are being explored.
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