Week 7 Post 3

By the end of the day today, I felt exhausted completely. I feel like even though I like exploring and learning new things, it tires me faster as I have to expand my memory and understanding of the hard concepts more. Today I have been trying to follow the examples for KNN and SVM classification methods, and it was hard for me to grasp the basics. It may also be because it is the end of the week, and I just feel like resting more than working. But it was still fun to try out new things. KNN classification algorithm is a non-parametric, supervised learning classifier which uses proximity to make classifications or predictions about the grouping of an individual data point. This is the definition of the KNN from the IBM website. It was interesting for me to find out that not only is it used as an algorithm for classification but also for regression problems. I do not understand the concept that is given for regression, but for classification, it basically means working off the assumption that similar points can be found near one another. So, for a given data point in the set, the algorithms find the distances between this and all other K numbers of datapoint in the dataset close to the initial point and votes for that category that has the most frequency. It is still difficult for me to explain the concept, which indicates that I am still not very familiar with it, but I am trying my best to learn it, and that makes me proud. Along with feeling exhausted, I feel inspired because I have never thought that I could go this far in understanding and working with my own dataset while working only by myself. Such an achievement puts a smile on my face at the end of the day and makes my whole experience at Purdue University worth it.

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