# Our Latest Articles

- Ridge Regressionby vaishanavi vaishanaviRidge 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…
- Logistic Regression (now with the math behind it!)by Ishan ShishodiyaLogistic 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…
- Backward Propagation in Artificial Neural Networkby Vidit ShethBackpropagation 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….
- Anomaly Detectionby SakshiAnomaly 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…
- Linear Regressionby Ishan ShishodiyaWhat 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…
- Affinity Propagation Algorithmby Shruti VermaIntroduction 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…
- 10 datasets for beginnersby Ishan ShishodiyaAs a beginner, learning Machine Learning and Data Science can be a mountain of a task. Thankfully there exist a few datasets which help you in building confidence and honing your skills! Here are 10 datasets that I think are suited for beginners – 1. Beginner’s Classification Dataset It’s as…
- 10 awesome ML datasets that deserve your attention!by Sourabh GuptaA list of not-so-popular machine learning articles that are highly useful yet underrated
- Mean, Median, and Mode (now with Python!)by Ishan ShishodiyaMean, median, and mode are the most commonly used measures for central tendencies in descriptive statistics. Everyone learns them in school…so I hope you paid attention back in your school days because it’ll be very relevant in this article. But even if you didn’t, don’t worry, that’s what I am…
- Analysis of Covid-19 in India and its reasonsby Ishan ShishodiyaThe 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…