Our Blogposts

Data generation process significantly impacts model performance; understanding it is crucial for choosing the right loss function and model type in machine learning, as demonstrated through a simple linear regression example with varying degrees of freedom in error distribution in this article.

This article discusses about Simple Linear Regression with Neural Network: Understanding, Coding, and Plotting Loss over Epochs,

This article explains preprocessing of numerical and categorical features for input into a deep neural network in details with the mathematics behind it.

This article helps you in fundamental data understanding, model assessment, and hands-on learning in basic predictive data science with logistic regression in R and Python.

Data science aims to uncover intricate, hidden patterns in data. Data alone is powerless; questions drive its transformation into meaningful insights, as illustrated in the scientific process. This is addressed in this article.

Machine learning involves computers learning patterns from data, unlike following explicit instructions. While instructions work for simple problems, learning is essential for complex ones. Neither is ‘better’; context matters. We explore this in this article.