In this post, we will discuss about the various Machine Learning Algorithm. The main objective of this blog is to give you a basic level of understanding on what are the different types of Machine Learning Algorithm present. There are many algorithms and it might seem a bit overwhelming to see a bunch of them,…Read more
Introduction to Bias and Variance Bias and Variance plays a very important role while building a model. To frame it in simple terms Bias is interpreted as the model error encountered for the training data and Variance is interpreted as the model error encountered for the test data. To understand the concept of Bias and…Read more
Introduction In this post we will try to understand about regularization and hyperparameter-tuning using Ridge and Lasso Regression. Before that we need to understand few concepts of Linear Regression. I will provide a brief explanation here which would suffice our motive of this topic, however if you want to get a more in-depth understanding of…Read more
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
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
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
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