Reinforcement Learning is an area of machine learning which teaches us to take actions to maximize rewards in a particular situation. Reinforcement learning is used in a various of fields, starting from Automobile to Medicine and many others. In Reinforcement Learning, the agent is not aware of the different states, the actions available in all…Read more
Machine Learning
Reinforcement Learning
Reinforcement learning is a field of Machine Learning where software agents in order to solve a particular problem takes action in an uncertain and potentially complex environment. Through these actions, the software agent learns to achieve a goal. Reinforcement Learning is one of the 3 machine learning paradigms alongside supervised and unsupervised learning. The main…Read more
Machine Learning Algorithm
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
Classification Metrics in Machine Learning
Introduction Choosing the right Classification Metrics is very crucial for model evaluation. Metrics like Confusion Matrix is a simple yet a very powerful Classification Metrics when it comes to evaluating the performance of a classification problem. Confusion Matrix is a performance measurement for machine learning problem where output can be two or more classes. Similarly…Read more
Multicollinearity with Ordinary Least Squares(OLS)
Introduction Ordinary Least Squares is a method which helps us estimate the unknown parameters in the Linear regression model. How does it estimate the parameters though? Well, it estimates the parameters by minimizing the sum of squared residuals. The way it does is , it draws a line through the data points such that the squared…Read more
Bias and Variance in Machine Learning
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
Ridge and Lasso Regression
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
Cross Validation techniques and its applications
Introduction Before getting into the details of Cross Validation techniques and its application, we will see what the steps in a Machine Learning Pipeline are. This will help us to better visualize the purpose of doing Cross Validation. To understand Cross Validation, we need to know couple of things that are involved in model creation….Read more
Math behind Simple Linear Regression
Simple Linear Regression In this post we will try to understand the Math behind Simple Linear Regression. But before getting into the details let’s understand what Simple Linear Regression means. Simple Linear Regression basically defines relationship between one feature and a continuous outcome variable/ dependent variable y = α +βx. This equation is similar to the…Read more
Covariance and Correlation
Introduction In this post, we will discuss about Covariance and Correlation. This plays an important role while doing feature selection. Covariance, as the name suggests is the measure of variance of 2 variables when they are taken together. When we have one variable then we call it as variance, but in case of 2 variables we specify it as…Read more