A large number of features can be both a blessing or a boon depending upon the instance. It happens so quite often that quite a few features available are correlated and hence redundant. Larger dimensional data are harder to visualize. This is where dimensionality reduction techniques come in handy. PCA…


Perhaps I should start with an apology for the terrible puns eh?

Anyway, Bagging is one of the two major sub-classes of ensemble machine learning techniques(I shall explain ensembling in this context is in a while now), with the other being Boosting. It can be used for both classification and…


When everything is said and done, when the data has been analysed and features have been tinkered with, when the hyperparameters have been tuned and the model is ready and you finally have your prediction, we are faced with a need to evaluate this prediction. Now while we have a…


Let’s say you live in the era before phones were invented and you wanted to plan an outing with your friends living far apart. Can you imagine the efforts you’d need to take to communicate? How in a group of say five, one person has to synchronize the entire communication…


When introduced to unsupervised machine learning algorithms, one of the first algorithms one learns is k-means clustering. It’s a neat algorithm which can provide some really useful insights about the data. The intuition behind the algorithm is simple. Given a data in some feature space, we find groups of similar…


I’ve observed that just like me, a lot of us who try to learn about support vector machines find it difficult to comprehend the brilliance of kernels. It took me quite some time and a lot of resources but I’ve finally crossed the river and I intend to help you…


HELLO WORLD! Oh yes I am indeed a programmer. Well trying to be one anyway.

Life’s too short to learn from one’s own mistakes alone, right? So well in this blog and in the ones to come, I intend to share the mistakes I make, and the lessons I learn…

Harish Kandan

Aspiring data scientist. Passionate for physics and mathematics. Have a special love for poems.

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