This tutorial is a set of 15 hour videos by acclaimed experts in the area of machine learning and statistics from Stanford. It is suitable for Beginners as well as experts Continue reading “Tutorial : Beginner to advanced machine learning in 15 hour Videos”
In this tutorial we will learn how to interpret another very important measure called F-Statistic which is thrown out to us in the summary of regression model by R.
This tutorial talks about interpretation of the most fundamental measure reported for models which is R Squared and Adjusted R Squared. We will try to give a clear guidelines for interpreting R Squared and Adjusted R Squared
In this tutorial we will discuss about effectively using diagnostic plots for regression models using R and how can we correct the model by looking at the diagnostic plots.
In this tutorial we will learn a very important aspect of analyzing regression i.e. Residual Analysis. Residual Analysis is a very important tool used by Data Science experts , knowing which will turn you into an amateur to a pro.
Continue reading “R Tutorial : Residual Analysis for Regression”
This tutorial goes one step ahead from 2 variable regression to another type of regression which is Multiple Linear Regression. We will go through multiple linear regression using an example in R
In this tutorial we will try our hands on a very basic 2 variable linear regression using R. We will also learn how to interpret output given by R and tryout various visualizations required for interpreting simple Linear regression.
In this tutorial we will discuss about structure of Linear regression and how a Linear regression Equation is constructed for 2 variable model.
This Tutorial talks about basics of Linear regression by discussing in depth about the concept of Linearity and Which type of linearity is desirable.
This was a research paper that we submitted to ICAPADS-2012 an IEEE – Institute of High performance distributed computing conference . It talks about a map reduce based solution to maze traversal problem which is applicable in many practical problems.