EP 3. APPROACHES TO MACHINE LEARNING(PART 2):

EP3  APPROACHES TO ML(PART 2):

In the last post we have seen how supervised learning performs to get the output in 2 ways i.e in continuous valued output and discrete valued output. Now without any delay let's get started with Unsupervised learning. Unsupervised learning is best suited when the problem requires a massive amount of data which is unlabeled. This data is actually un readeable or not really intresting to see. For example , Social media applicants such as twitter,Instagram,facebook and many social media applicants have lot amounts of unnamed data. By applying unsupervised machine learning algorithms, the system will learn itself from its own mistakes through many attemps and use cases. Some of the intresting examples where we apply machine learning algorithms is Self driving cars and also drones. 

In Unsupervised machine learning we actually hear a lot about clustering. When you came across google news, You actually find the same data which is published by many publishers which actually has the same content but differs in the quality of the news pattern. So here we can see the same data occuring in many segements or parts is know as clustering.Unsupervised Learning algorithms segments data into groups of examples(clusters) or group of features.The unlabelled data creates the parameter values and classification of the data.There are many examples where we ca actually probe that an Unsupervised machine learning algorithm approach can help in determining the outcomes more quickly than a supervised machine learning algorithm approach.The below graph has no labels corresponded and it comes under unsupervised learning.


AUTHOR: Kaushik Tummalapalli

THANK YOU! HOPE IN SEE YOU GUYS IN NEXT EPOSIDE! HAVE A GREAT DAY!











Comments

  1. Sowmya16 September 2020 at 22:21

    nice information on data science has given thank you very much.

    ReplyDelete

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