Ensemble learning is a technique that combines multiple models to produce predictions that are more accurate, robust, and reliable than those from any single model. By aggregating the strengths of

XGBoost, short for eXtreme Gradient Boosting, is a high-performance implementation of gradient-boosted decision trees designed for speed, accuracy, and scalability on structured/tabular data. It builds an ensemble of shallow decision

A linear regression model learns a straight line or hyperplane that best predicts a target from a set of input features. The prediction is a weighted sum of features plus

Swarm intelligence refers to intelligent behavior that emerges from the collective actions of many simple, decentralized agents—whether animals like ants and birds or artificial systems like robots and software agents—without

Machine learning models are powerful tools. Their true value unlocks with deployment. A deployed model makes predictions in the real world. This integration solves actual problems. Modern deployment uses MLOps

Federated learning is a technique for training a shared machine learning model across many devices or organizations while keeping their raw data in place. Instead of sending personal or sensitive

Training a logistic regression model involves teaching it to predict a binary outcome—typically labeled as 0 or 1—based on input features. It’s a foundational algorithm in machine learning, especially useful

Random Forest is a powerful machine learning algorithm that belongs to the family of ensemble methods. It is primarily used for classification and regression tasks and is known for its

Building a self-driving car simulation involves creating a virtual environment where algorithms can perceive surroundings, make driving decisions, and control vehicle movements. This simulation serves as a crucial testing ground

Splitting data into training, validation, and test sets is a fundamental step in developing reliable machine learning models. The purpose of this split is to ensure that the model learns