Ridge Regression is a Linear Regression model use to solve some of the problems of Ordinary Least Squares by imposing penalty on regression coefficients. What is Ridge Regression? We have seen Ordinary Least Squares. Suppose we have independent variable X and dependent variable Y. we can write and our Objective Function is Suppose we have…
Logistic Regression is a type of linear model that’s mostly used for binary classification but can also be used for multi-class classification. If the term linear model sounds something familiar, then that might be because Linear Regression is also a type of linear model. To proceed with this notebook you first have to make sure that you understand ML concepts…
Backpropagation is the tool of neural network training. It is a way to adjust the neural network weights based on the error rate obtained in the previous iteration. Proper adjustment of weights can reduce the error rate and increase the generalizability of the model.Backpropagation in neural networks is a shortened form of “error propagation”. This is the standard method for training artificial neural networks. This method helps to calculate the slope of the loss function for all weights in the network. The neural network backpropagation algorithms use…
Anomaly detection or outlier detection is identifying data points, events, or observations that deviate significantly from the majority of the data and do not follow a pre-defined notion of normal behavior. It is carried out to prevent fraud and to create a secure system or model. But before we talk about anomaly detection, let us…
What is Linear Regression? Linear regression quantifies the relationship between one or more predictor variables and an outcome variable. It is commonly used for predictive analysis and models. For example, it can be used to quantify the relative effects of age, gender, and diet (the predictor variables) on height (the outcome variable). It is also known as multiple regression, multivariate regression, ordinary least squares (OLS),…
Introduction Affinity Propagation was first published in 2007 by Brendan Frey and Delbert Dueck and It is only getting more and more popular due to its simplicity, general applicability, and performance. Affinity Propagation is an unsupervised machine learning algorithm unlike clustering algorithms such as K means clustering. The main drawbacks of K-Means and similar algorithms…
The COVID-19 pandemic became one of the worst things that ever happened in India. It caused over 3 million deaths throughout the nation and left a mark on the economy that would take a few years to heal. COVID-19 The COVID-19 pandemic, also known as the coronavirus pandemic, is an ongoing global pandemic of coronavirus…
So… Ridge Regression is a modified version of Linear Regression. and a classic example of regularization using L2 penalty. So to learn about Ridge Regression, you have to make sure you understand Linear Regression. If you don’t then click here. If you don’t know what Gradient Descent is, then click here. It is an absolute must that…
Buckle up Buckaroo because Gradient Descent is gonna be a long one (and a tricky one too). The whole article would be a lot more “mathy” than most articles as it tries to cover the concepts behind a Machine Learning algorithm called Linear Regression. If you don’t know what Linear Regression is, go through this article once. It would help…
TF-IDF is short for Term Frequency-Inverse Document Frequency. It is a vectorization technique used in the field of Natural Language Processing. Yes I know, it is a daunting looking phrase, but trust me, it’s a lot simpler than it sounds. Uses of TF-IDF Natural Language Processing or NLP is the field in Machine Learning that…
