Principal Component Analysis is one of the bread and butter dimensionality reduction methods for unsupervised learning. One of the assumptions of PCA is that the data is linearly separable. Kernal PCA, is a variant of PCA that can handle non-linear data and make it linearly separable. If you wonder what is linearly separable, Python Machine […]
Data Science
Data Science with R and Python- A Round Up: February 2020
We are back with February 2020’s “Data Science with R and Python Round Up”. The new year resolution is that to continue the monthly round up compiling compile interesting news, Python, R blog posts on anything related to learning, data, data science, ML and AI. It just hopes to offer one more chance catch up […]
Data Science with R and Python- A Round Up: January 2020
Here you go with the first post for the year on “Data Science with R and Python Round Up”. The new year resolution is that to continue the monthly round up. This roundup is an attempt to compile interesting news, Python, R blog posts on anything related to data, data science, ML and AI. Hopefully […]
Data Science with R and Python- A Round Up: December 2019
One of the good things of being active in twitter is that you get exposed to interesting content. The bad thing about it is that you retweet with the goal of finding it later and read it. However, you never find it or read it afterwards. So, here is a new attempt to catch up […]
31 Days of #rstats Resources Advent
R community is amazing with sharing excellent learning resources for free. And it seems never ending!. If you are like me, you might have missed a lot of “#rstats resource advent” daily tweets containing fantastic R resources from RStudio’s Mara Averick (@dataandme). Not to worry, you have all the #rstats resource advent tweets from Day […]