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Machine Learning Basics (Part-2)

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Machine learning is a branch of artificial intelligence. Using computing, we design systems that can learn from data in a manner of being trained. The systems might learn and improve with experience, and with time, refine a model that can be used to predict outcomes of questions based on the previous learning.

Life cycle of machine learning:
  • Acquisition . Collect the data
  • Prepare - Data Cleaning and Quality
  • Process- Run Machine Tools
  • Report- Present the Results


You can acquire data from many sources; it might be data that's held by your organization or open data from the Internet. There might be one data set, or there could be ten or more.

You must come to accept that data will need to be cleaned and checked for quality before any processing can take place. These processes occur during the prepare phase.

The processing phase is where the work gets done. The machine learning routines that you have created perform this phase.

Finally, the results are presented. Reporting can happen in a variety of ways, such as reinvesting the data back into a data store or reporting the results as a spreadsheet or report.

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