Machine learning deals with various kinds of problems. In fact, almost all fields have a scope to be automatized or improved with the help of machine learning. A few such problems on which a great deal of work is being done are given below.
Time Series Data
This is the data which changes according to time, and hence time plays a crucial role in it, which we largely discussed in this tutorial.
Non-Time Series Data
It is the data independent of time, and a major percentage of ML problems are on nontime series data. For simplicity, we shall categorize it further as −
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Numerical Data − Computers, unlike humans, only understand numbers, so all kinds of data ultimately is converted to numerical data for machine learning, for example, image data is converted to (r,b,g) values, characters are converted to ASCII codes or words are indexed to numbers, speech data is converted to mfcc files containing numerical data.
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Image Data − Computer vision has revolutionized the world of computers, it has various application in the field of medicine, satellite imaging etc.
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Text Data − Natural Language Processing (NLP) is used for text classification, paraphrase detection and language summarization. This is what makes Google and Facebook smart.
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Speech Data − Speech Processing involves speech recognition and sentiment understanding. It plays a crucial role in imparting computers the human-like qualities.