Supervised Learning and Unsupervised Learning are two of the most widely used machine learning methods. However, there are other methods as well and in this article we are going to discuss about some other popular machine learning methods. Supervised Learning: As the name suggests, there is some sort of supervision involved in this set of machine learning algorithms. Here, the algorithm is trained using labeled data. For example, consider a […]

This is the second article in our machine learning for beginners’ series. In the last article, we discussed regarding the general idea of Machine Learning. In this article we will go one step further and discuss regarding Naïve Bayes Classification. Naïve Bayes Classification Algorithm is a supervised machine learning technique that is used to solve classification problems. Bayes Theorem (posterior probability) forms the core part of the Naïve Bayes algorithm. […]

This is the introductory post in the series of articles on machine learning that I am going the share in coming days. My objective is to help you understand the domain of machine learning and the different tools that are available for us to solve real life problems. Lets enter into the worlds of Machine learning!! If you are already familiar with some basics of machine learning then you may […]

Majority of the quantitative research studies that are undertaken by academician and scholars will be one of three types, which we refer to as duplication, generalization or extension. Duplication research studies take some published research and then repeat it under identical scenario and check if the results obtained are same as in the original research study. In some cases the researcher might just use the original data and reanalyze to […]

The term multiple regression was first used by Pearson in 1908. Multiple regression is a statistical technique that help us in understanding about the relationship between several independent or predictor variables and a dependent variable (also called criterion variable). Let us take an example to understand this in some detail. Suppose that you are a researcher and you record data of 1000 MBA graduates about their gender, age, ethnicity, high school […]