Hearted Youtube comments on Simplilearn (@SimplilearnOfficial) channel.
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**Summary**:
- machine learning is the general term for when computers learn from data
- there are lots of different ways ("algorithms") that machines can learn
- the algorithms can be grouped into supervised, unsupervised, and reinforcement algorithms*
- the data that you feed to a machine learning algorithm can be input-output pairs or just inputs
- supervised learning algorithms require input-output pairs (i.e. they require the output)
- unsupervised learning requires only the input data (not the outputs)
- here is how, in general, supervised algorithms work:
- you feed it an example input, then the associated output
- you repeat the above step many many times
- eventually, the algorithm picks up a pattern between the inputs and outputs
- now, you can feed it a brand new input, and it will predict the output for you
- here is how, in general, unsupervised algorithms work:
- you feed it an example input (without the associated output)
- you repeat the above step many times
- eventually, the algorithm clusters your inputs into groups
- now, you can feed it a brand new input, and the algorithm will predict which cluster it belongs with
* the first example in this video used the k-nearest neighbor algorithm, which is a supervised machine learning algorithm
Hope that was useful to someone!
Thanks for the video, really enjoyed it!! :)
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