If you're building machine learning models for solving different prediction problems, you've probably heard of clustering. Clustering is a popular…
Eigenvalues and eigenvectors are important concepts in linear algebra that have numerous applications in data science. They provide a way…
Z-score, also known as the standard score or Z-statistics, is a powerful statistical concept that plays a vital role in…
Descriptive statistics is a branch of statistics that deals with the analysis of data. It is concerned with summarizing and…
Artificial Neural Networks (ANN) are a powerful machine learning / deep learning technique inspired by the workings of the human…
Support vector machines (SVM) are a popular and powerful machine learning technique for classification and regression tasks. SVM models are…
Today, when organization is aiming to become data-driven, it is imperative that their data science and product management teams understand…
Aspiring data scientists and AI enthusiasts in India have a plethora of opportunities in store, thanks to the country's booming…
Regression models are an essential tool for data scientists and statisticians to understand the relationship between variables and make predictions…
Have you ever told a story to someone, but they just didn't seem to understand it? They might have been…
Classification models are a fundamental part of machine learning and are used extensively in various industries. Evaluating the performance of…
The degree of freedom (DOF) is a term that statisticians use to describe the degree of independence in statistical data.…
Forecasting is the process of predicting future events based on past and present data. Time-series forecasting is a type of…
Support Vector Machines (SVMs) are a powerful and versatile machine learning algorithm that has gained widespread popularity among data scientists…
Have you ever wondered what fixed effect, random effect and mixed effects models are? Or, more importantly, how they differ…
As data scientists, we are constantly exploring new techniques and algorithms to improve the accuracy and efficiency of our models.…