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Note for Machine Learning - ML By ganesh kavhar

  • Machine Learning - ML
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Consider an example: There’s a Shopping Mart Owner who conducted a survey for which he has a long list of questions and answers that he had asked from the customers, this list of questions and answers is DATA. Now every time when he want to infer anything and can’t just go through each and every question of thousands of customers to find something relevant as it would be time-consuming and not helpful. In order to reduce this overhead and time wastage and to make work easier, data is manipulated through software, calculations, graphs etc. as per own convenience, this inference from manipulated data is Information. So, Data is must for Information. Now Knowledge has its role in differentiating between two individuals having same information. Knowledge is actually not a technical content but is linked to human thought process. Properties of Data – 1. Volume : Scale of Data. With growing world population and technology at exposure, huge data is being generated each and every millisecond. 2. Variety : Different forms of data – healthcare, images, videos, audio clippings. 3. Velocity : Rate of data streaming and generation. 4. Value : Meaningfulness of data in terms of information which researchers can infer from it. 5. Veracity : Certainty and correctness in data we are working on. Some facts about Data:  As compared to 2005, 300 times i.e. 40 Zettabytes (1ZB=10^21 bytes) of data will be generated by 2020.  By 2011, healthcare sector has a data of 161 Billion Gigabytes  400 Million tweets are sent by about 200 million active users per day  Each month, more than 4 Billion hours of video streaming is done by the users.  30 Billion different types of contents are shared every month by the user.  It is reported that about 27% of data is inaccurate and so 1 in 3 business idealists or leaders don’t trust the information on which they are making decisions. The above-mentioned facts are just a glimpse of the actually existing huge data statistics. When we talk in terms of real world scenario, the size of data currently present and is getting generated each and every moment is beyond our mental horizons to imagine.

Lecture Notes