Introduction Understanding the math behind any algorithm is very important. Often we dive straight into solving a problem using some machine learning algorithm and applying all sorts of techniques without understanding the underlying fundamentals of the algorithm, the assumptions that should be taken while implementing it on a particular scenario etc.In this post, we will…Read more
Machine Learning
Linear Regression
The figure that you are seeing above details the various steps in data preprocessing as well as the Linear regression. We have covered the data preprocessing steps in detail here. In this post we will be going over in detail on the lower portion of the figure which is the Linear Regression. Regression Before getting…Read more
Data Visualization using Functional/Object Oriented approach
Introduction Data Visualization is the graphical representation of data which helps us in storytelling. This can be seen mostly as an art and science. To communicate the results and findings in a better and comprehensive way we usually use plots, charts, statistical graphics and other tools. Data Visualization is a very powerful tool. It will enable…Read more
Introduction to Machine Learning
This blog will be more theoretical introduction towards what is Machine Learning and its different paradigms. So, let’s take a typical definition of Machine Learning and then break it down to understand what it means: “A machine or agent is said to learn from experience with respect to some class of tasks, and a performance…Read more
Data Visualization
This post will mostly cover ways of visualization of data using predefined statements and passing different parameters associated with the plots. However, if you want to learn the more functional and object oriented way of visualizing the data, then refer this post. Some of the visualizations that we will cover in this post are as…Read more
Data Preprocessing | Data Cleaning Python
Introduction Data Preprocessing is the most important step when we are building our model. In Data Preprocessing step, the data is transformed into a form where it becomes suitable for model ingestion. There are various steps involved in Data Preprocessing are shown below in the flowchart. In this post we will cover only the first step…Read more
Creating Feature and Target Matrix
Before creating any model, the first and foremost thing that we generally do is create the feature and target matrix. Let’s see how we will do that. Before that, let’s understand our dataset which was taken from Kaggle: Also refer this post to see how we implement an algorithm after selecting the Feature and Target…Read more
Regression Metrics
Introduction This post will be more theoretical and would explain in detail about the different Regression Metrics involved in Regression Models and what are their advantages and disadvantages. While we discuss about the different Regression Metrics in this post, take a while to also go through this post which discusses about the mathematical assumptions we…Read more
Simple Linear Regression
Introduction What is Simple Linear Regression Simple Linear Regression basically defines the relation between a one feature and the outcome variable. This can be specified using the formula y = α + βx which is similar to the slope-intercept form, where y is the value of the dependent variable, α is the intercept β denotes…Read more