Supervised learning

learningको लागि तस्बिर परिणामSupervised learning stands for training a computer or a machine or we can say feeding the data to an algorithm so that the program can have a close to accurate guess.
If you want to distinguish between apple and orange, only the color and texture is not sufficient. We need to first train the program with training data set and this helps to analyze all the texture, color and pattern of the all the different pictures of the training data set and can help to recognize the fruit to a higher degree though not accurate.
Example: Facebook  uses your photos from your profile or the tagged photos and trains the program thus training data set and can recognize you from the photo given.
Another example would be if you can extract the information and import it to the program about the temp and genre of the music you like. Then it can definitely recommend a new song.
Stock markets can also be analyzed from the same method so this is a very useful method.
Example of non-supervised learning is like analyzing the bank data for false transactions and flag the fraud is not supervised learning.

Thus if you can feed some information to the program with the training data set, you can easily find the closest match to the unknown data by analyzing the training dataset. 

I will be posting about Machine Learning every week for few years. So please stick by and share these posts. I assure you will learn a lot with the help of my blog.
Email me: ajay.banstola@gmail.com for any queries. Thanks.

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