CLASSIFICATION

Hello, welcome to my blog. In this post I am going to talk about another popular application of machine learning – classification. First, let me define classification. It is the allocation (or organization) of items into groups (or categories) according to type. In the context of machine learning, classification is using the features of an item to predict what class (out of a two or more classes) it belongs to. It is one of the fundamentals tools of machine learning.

WORD CLOUD VISUALIZATION IN R

Hello, welcome to my blog. In this post I want to demonstrate how to create a word cloud using the R programming language. For more information on the R programming language click here. A word cloud is an image composed of words used in a particular text or subject, in which size of each words indicates frequency or importance. 

THE BIAS-VARIANCE TRADEOFF

Hello, welcome to my blog. In this post, I want to talk about the Bias-Variance trade-off which is a very important topic in Machine Learning. Before I do that let me lay the foundation. In my post on Linear Regression, I said that the goal of a linear regression model is to find parameters for our linear regression line that minimize the error between our predictions and the actual observations.

IMPLEMENTING NEAREST NEIGHBOURS IN PYTHON

Hello, welcome to my blog. In the last post I introduced the concept of nearest neighbours and how it can be used to for either prediction or classification. In case you have not read it, you can read it here. 

In this post, I will show how to implement nearest neighbours in Python. This time it will be a little different because I will use my own code instead of a library (like I did in other posts). sklearn (a Python library) provides an implementation of nearest neighbours but I think it better if I implemented it myself so I can explain what is really happening in the program.