Linear Regression is one of the commonly used statistical techniques used for understanding linear relationship between two or more variables. It is such a common technique, there are a number of ways one can perform linear regression analysis in Python. In this post we will do linear regression analysis, kind of from scratch, using matrix… Continue reading Linear Regression Using Matrix Multiplication in Python Using NumPy
Category: Machine Learning
Introduction to Probabilistic Programming with PyStan
Stan, developed by a team led by Andrew Gelman, is one of the leading languages to do probabilistic computing. The core of probabilistic computing lies in Bayesian statistics. Stan gets its name in honor of Stanislaw Ulam, co-inventor of the Monte Carlo method, the computational engine behind all Bayesian computing. Stan is C++ package providing… Continue reading Introduction to Probabilistic Programming with PyStan
Introduction to Linear Regression in R
Linear Regression is one of the most commonly used statistical methods. Linear modeling and Linear regression helps us understand the relationship between multiple variables. In the simplest case, linear regression is about understanding the relation between two variables, X and Y. One of the ways to understand linear regression is that we have the observed… Continue reading Introduction to Linear Regression in R
Introduction to Linear Regression in Python
Linear regression is one of the most commonly used statistical technique to understand relationship between two quantitative variables (in the simplest case). Simple linear regression models relationship between two variables X and Y, where X and Y are vectors with multiple values. For example, X could how well each country is doing economically, like GDP… Continue reading Introduction to Linear Regression in Python
Dimensionality Reduction with tSNE in Python
tSNE, short for t-Distributed Stochastic Neighbor Embedding is a dimensionality reduction technique that can be very useful for visualizing high-dimensional datasets. tSNE was developed by Laurens van der Maaten and Geoffrey Hinton. Unlike, PCA, one of the commonly used dimensionality reduction techniques, tSNE is non-linear and probabilistic technique. What this means tSNE can capture non-linaer… Continue reading Dimensionality Reduction with tSNE in Python