What does simulation mean? Well, it says, we are using a model (Mathematical representation of a system) where the computer is used to act like a given system. And we can see a simulation type called Monte Carlo simulations. It just uses internally generated (pseudo) random numbers for the simulation.

Support Vector Machines (SVMs) are a versatile tool in machine learning, capable of handling both linear and complex non-linear data through the use of kernel functions. Kernels allow SVMs to transform data into higher dimensions, making it easier to find optimal decision boundaries. In this blog post, we will: 1.

What are Support Vector Machines (SVMs)? Support Vector Machines (SVMs) are powerful tools used in supervised machine learning for tasks like classification, regression, and finding outliers (data points that don't fit the general pattern). Think of them as a way to draw a line (or a curve) that

Categorical data refers to variables that represent categories or labels rather than numerical values. For example, "color" could have categories like "red," "blue," and "green." Machine learning algorithms, however, typically require numerical input to perform computations. Converting categorical data into numerical form

What is Feature Scaling? Imagine you’re setting up a race between a cheetah and a tortoise. If you measured their speeds in different units—miles per hour for the cheetah and meters per second for the tortoise—it would be tough to compare their performance. In machine learning, feature

In the previous article, I explained cross-validation. Now, I’m going to show you K-fold cross-validation in action. This is one of the most common techniques in cross-validation. Remember, if your dataset is imbalanced, you should first address this issue before using any cross-validation technique. K-fold cross-validation involves splitting your

Cross-validation is a technique for evaluating machine learning models by training them on different subsets of the available data and testing them on the remaining data. This approach helps to detect overfitting, which occurs when a model performs well on training data but fails to generalize to new, unseen data.

Here, we’re diving into the world of hyperparameter tuning in machine learning. If you’re new to this topic, don’t worry. We’ll break it down step by step with simple explanations and code examples. We’ll also cover why tuning hyperparameters is important and explore different methods

Finding the right machine learning model can feel like finding a needle in a haystack. With so many options out there, how do you know which one to choose? Don’t worry! This guide will help you figure it out by breaking down the process into simple steps, using the

In the world of artificial intelligence (AI), several terms have become very popular, especially with the rise of advanced tools like ChatGPT. Let's break down these terms to understand what they mean, why they're important, and how they're used. I’ll keep it simple