Gaussian Mixture Models related concepts are explained in the notebook below. Please practice and ask your questions in the forum if any. Please don't share the notebook anywhere without our ...

In this tutorial, we will learn our next machine-learning model called nearest neighbors (NN), known as k-nearest Neighbors (k-NN). It belongs to the family of supervised learning algorithms, specifically within ...

Welcome to a beginner's guide to data preprocessing in machine learning. In the previous few tutorials, we saw some basic concepts and a few models related to Linear Regression. In ...

If you have been following my previous tutorials, you must be familiar with other optimization techniques to solve ML problems. In this tutorial, I am going to introduce you to ...

In our last tutorial, we discussed naive Bayes, a nifty tool for categorizing things. Now, let's delve into logistic regression, another heavyweight in the world of classification. So, why go ...

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 ...