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Introduction to AI Types of Machine Learning and their Applications Here is a list of commonly used ML algorithms and their respective category: Supervised Learning Algorithms: Linear Regression: A statistical method used for predicting continuous variables based on a linear relationship between input variables and the output variable. Logistic Regression: A statistical method used for binary classification problems where the output is a probability value ranging from 0 to 1. K-Nearest Neighbors (KNN): A simple classification algorithm that predicts the class of an input by finding the k-nearest neighbors to that input. Decision Trees: A decision-making algorithm that splits data into hierarchical structures based on a set of rules that best classify the input data. Random Forest: An ensemble learning algorithm that combines multiple decision trees to reduce overfitting and improve accuracy. Support Vector Machines (SVM): A binary classification algorithm that maximizes the margin betw

Introduction to AI Overview of AI, Machine Learning, and Deep Learning Artificial Intelligence (AI) refers to the ability of machines to perform tasks that typically require human intelligence, such as understanding natural language, recognizing objects in images, and making decisions based on data. AI can be classified into two categories: narrow or weak AI and general or strong AI. Narrow AI is designed to perform specific tasks, while general AI is designed to perform any intellectual task that a human can do. Machine Learning (ML) is a subset of AI that involves training algorithms to recognize patterns in data and make predictions or decisions without being explicitly programmed. There are three types of ML: supervised learning, unsupervised learning, and reinforcement learning. In supervised learning, the algorithm is trained on a labeled dataset to predict outputs for new inputs. In unsupervised learning, the algorithm identifies patterns and structures in unlabeled data. In r