An Overview of Linear Regression Models

Modeling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables) is commonly referred as a regression problem. The simplest model of such a relationship can be described by a linear function - referred as linear regression.

A Practical guide to Autoencoders

Usually in a conventional neural network, one tries to predict a target vector \$y\$ from input vectors \$x\$. In an auto-encoder network, one tries to predict \$x\$ from \$x\$. It is trivial to learn a mapping from \$x\$ to \$x\$ if the network has no constraints, but if the network is constrained the learning process becomes more interesting. In this article, we are going to take a detailed look at the mathematics of different types of autoencoders (with different constraints) along with a sample implementation of it using Keras, with a tensorflow back-end.

Understanding Boosted Trees Models

In the previous post, we learned about tree based learning methods - basics of tree based models and the use of bagging to reduce variance. We also looked at one of the most famous learning algorithms based on the idea of bagging- random forests. In this post, we will look into the details of yet another type of tree-based learning algorithms: boosted trees.

A Practical Guide to Tree Based Learning Algorithms

Tree based learning algorithms are quite common in data science competitions. These algorithms empower predictive models with high accuracy, stability and ease of interpretation. Unlike linear models, they map non-linear relationships quite well. Common examples of tree based models are: decision trees, random forest, and boosted trees.

Understanding Support Vector Machine via Examples

In the previous post on Support Vector Machines (SVM), we looked at the mathematical details of the algorithm. In this post, I will be discussing the practical implementations of SVM for classification as well as regression. I will be using the iris dataset as an example for the classification problem, and a randomly generated data as an example for the regression problem.

Support Vector Machines

In this post we will explore a class of machine learning methods called Support Vector Machines also known commonly as SVM.