Have you ever wondered how an AI agent can learn to play a game—like Snake, Pac-Man, or even chess—just by trial and error? Behind the scenes, a powerful concept called Q-learning is often at work. Q-learning is a model-free reinforcement learning algorithm. That means the agent doesn’t need to know the rules of the environment—it learns them by experience.
Convolutional Neural Networks (CNNs) are a class of deep learning models that excel at working with image data. Instead of processing each pixel independently (like in a fully connected neural network), CNNs use filters (or kernels) to scan across the image, capturing spatial hierarchies and local patterns like edges, textures, and shapes.
Support Vector Machine (SVM) is a powerful supervised learning algorithm used for classification and regression tasks. It works by finding the optimal hyperplane that best separates data points of different classes while maximizing the margin between them. SVM is especially effective in high-dimensional spaces and robust to outliers.
Principal Component Analysis (PCA) is a widely used technique for reducing the dimensionality of datasets while retaining the most important information. It does this by transforming correlated variables into a smaller set of uncorrelated variables called principal components.
K-Means clustering is one of the simplest and most effective unsupervised machine learning algorithms. It is widely used for tasks such as customer segmentation and anomaly detection