Named Entity Recognition, or NER, is an NLP technique that detects and classifies important entities in text. Entities can include: Names of people Locations (cities, countries) Organizations Dates and times…

Word embeddings are a way to represent words as numerical vectors so that machines can understand their meanings and relationships. Unlike simple one-hot encoding, embeddings capture semantic similarity, meaning words…

Text Classification is a key NLP task where machines automatically assign predefined labels or categories to text. It’s used in spam detection, sentiment analysis, topic tagging, and more. Basically, it…

Natural Language Processing, or NLP, is a branch of AI that focuses on enabling machines to understand, interpret, and generate human language. It’s the technology behind chatbots, voice assistants, translation…

Principal Component Analysis, or PCA, is an unsupervised technique used to reduce the number of features in a dataset while preserving its most important patterns. Think of it as summarizing…

K-Means is a popular unsupervised learning algorithm used to group data points into clusters based on similarity.Unlike classification, it doesn’t use labels. Instead, it identifies patterns or natural groupings in…

K-Nearest Neighbors, or KNN, is a supervised learning algorithm used for classification and regression. It’s simple, intuitive, and easy to implement. KNN predicts the label of a new data point…

Support Vector Machine, or SVM, is a supervised machine learning algorithm mainly used for classification tasks but can also handle regression. SVM works by finding the best boundary (hyperplane) that…

Random Forest is like having a team of decision trees rather than relying on just one. Each tree makes a prediction, and the forest combines them to give a final…

A decision tree is one of the simplest yet powerful algorithms in machine learning. Think of it as a flowchart where each node asks a question, each branch represents the…