Imagine you are a real estate agent trying to estimate the price of a house based on its size. You've probably noticed that larger houses tend to have higher prices, ...

Probability calibration is the key to unlocking the true potential of machine learning models, ensuring that the predicted probabilities align with the actual likelihood of events. In this tutorial, we ...

In this tutorial, we will learn our next machine-learning model called Naive Bayes. Naive Bayes is widely recognized as a sophisticated and effective tool known for its simplicity. Rooted in ...

In this tutorial, we are going to learn an advanced concept related to decision trees called random forests. The idea behind random forests is the concept of ensembling. Sir Francis ...

In this post, we will learn about our next machine-learning algorithm, Decision Trees. Imagine you're trying to decide what movie to watch. You ask simple questions like "Do you want ...

In this post, we will learn about our next machine learning algorithm called support vector machine or SVM or support vector networks. This is a crucial concept and a powerful ...

In the previous post, we learned about PCA and how to use PCA in machine learning for various applications. In this tutorial, we are going to learn about linear discriminant ...

In the previous post, we learned about SVD and how to use SVD for low-rank approximation. Building upon the concepts of SVD now let's learn about principal component analysis and ...

The singular value decomposition (SVD) is a crucial concept for machine learning that builds the foundation of many algorithms you will use throughout your machine learning journey. It's not only ...