Artificial Intelligence

Supervised Learning: Understanding the Basics and Applications What is Supervised Learning ? Supervised learning is a popular technique in machine learning that involves training algorithms to predict outputs based on input data. It is called “supervised” because the algorithm is given labeled examples, which it uses to learn the relationship between inputs and outputs. The goal is to develop a model that can accurately predict outputs for new, unseen inputs. In this blog post, we’ll cover the basics of supervised learning, including key concepts, types of algorithms, how it works, and real-world applications. Concepts in Supervised Learning: In supervised learning, the algorithm is given a set of input-output pairs, called the training data. The inputs are typically represented as features, and the outputs are the labels. The algorithm uses this training data to learn a mapping function that takes inputs to outputs. This function can then be used to make predic

Machine learning branch and Types Machine learning is a branch of artificial intelligence that involves the use of algorithms and statistical models to enable computers to learn from data, without being explicitly programmed. There are several different branches of machine learning, each with their own unique characteristics and applications. Some of the main branches of machine learning include: 1 Supervised Learning 2 Unsupervised Learning 3 Reinforcement Learning 4 Semi-supervised Learning 5 Deep learning What is Supervised Learning Supervised learning is a type of machine learning in which an algorithm is trained on a labeled dataset, where the correct output is already known. The algorithm learns a function that maps input variables (also known as features or predictors) to the output variable (also known as the target or label). Once the algorithm has learned this function, it can be used to make predictions on new, unseen data. There are two main types of supervised l