Introduction to topic modeling using LDA (Latent Dirichlet Allocation)

Introduction In natural language processing, particularly text mining, topic modeling is a very important technique used commonly for identifying topics from a text source to enable informed decision making. Topic modeling is an unsupervised statistical modeling technique used for finding out a group of words, which collectively represent a topic in a large collection of documents. The article focusses on…

Introduction to Random Forest

Introduction: Random Forest Now that we have an idea about decision trees and how exactly they work, I think we can now go a step further and try to improve our decision tree models by introducing a very basic but very effective extension for decision trees, which are popularly known as “Random Forest”. To understand decision trees in detail, you…

All you want to know about Decision Tree Part 3

Decision Tree Part 3 This is the third article in the decision tree series, you can access other two here: Part 1: All you need to know about Decision Tree Part 1) Part 2: All you need to know about Decision Tree Part 2) In this previous article, I tried to construct a decision tree using R. For this, I have considered…

All you need to know about Decision Tree-Part 2

Introduction In my previous article, (All you need to know about Decision Tree Part 1), we have discussed on how a decision tree looks like, its terminology and we also have seen an example of a decision tree. Now that you got a basic idea on decision tree, in this article, I will discuss on some of the major concepts…

All you need to know about Decision Tree (Part-1)

Introduction As the title suggests, I’ll try to put necessary information on decision tree under this article. However, providing all the required information in one post will be difficult and makes you lost. So, I’ve made this article into three parts. Part 1 (this post) : we shall discuss introduction and definitions Part 2 :  Advanced topics related to decision…

Activation Functions in ANNs (Part-1)

Introduction In an ANN the activation function of a node is defined as the threshold after which the node will produce an output given an input or set of inputs. Activation functions can be linear or non-linear but mostly nonlinear functions are being used in ANNs. This is a very important in the way a network learns because in light…