Regression and Classification Machine Learning Algorithms Understanding the Differences Machine learning algorithms can be divided into two main categories: regression and classification. Regression is used when the output variable is continuous, while classification is used when the output variable is categorical. Regression Machine Learning Algorithms Regression machine learning algorithms are used to predict a continuous output variable. Some common regression algorithms include: Linear Regression: Linear regression is a simple algorithm that tries to model the relationship between the input variables and the output variable using a linear equation. It is commonly used in situations where there is a linear relationship between the input and output variables. Polynomial Regression: Polynomial regression is a variation of linear regression that can be used to model non-linear relationships between the input and output variables. It works by adding polynomial terms to the linear ...
Deep Learning: Understanding Neural Networks Deep Learning is a subfield of machine learning that involves the use of neural networks to model complex relationships in data. Neural networks are a series of interconnected nodes, or neurons, that process and transmit information. They are inspired by the structure and function of the human brain, and are capable of learning from large amounts of data without being explicitly programmed. Deep Learning has become increasingly popular in recent years due to its ability to handle complex and unstructured data, such as images, audio, and text. Some common applications of Deep Learning include computer vision, speech recognition, natural language processing, and autonomous vehicles. Neural networks can be divided into three main categories: feedforward neural networks, recurrent neural networks, and convolutional neural networks. Feedforward Neural Networks: Feedforward neural networks are the simplest type of neural network, consisting ...