Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables. The goal is to find a linear equation that best predicts the dependent variable based on the independent variables.
Logistic regression is used for binary classification problems where the goal is to predict the probability of a certain class or event occurring. Unlike linear regression, which predicts a continuous value, logistic regression predicts probabilities that fall between 0 and 1.
Decision trees are a popular machine learning model used for classification and regression tasks. They work by splitting the data into subsets based on feature values to make predictions.
Random Forest is an ensemble learning technique that combines multiple decision trees to improve predictive performance and robustness. It’s commonly used for both classification and regression tasks.
Support Vector Machines (SVM) are powerful supervised learning models used for classification and regression tasks. They work by finding a hyperplane that best separates the classes in a dataset.
Neural Networks (NNs) are a class of machine learning models inspired by the structure and functioning of the human brain. They are used for various tasks including classification, regression, and pattern recognition.
Long Short-Term Memory (LSTM) networks are a type of Recurrent Neural Network (RNN) designed to handle sequential data and overcome the limitations of traditional RNNs, such as the vanishing gradient problem. LSTMs are particularly effective for tasks involving sequences, such as time series forecasting, natural language processing, and speech recognition.
Gradient Boosting Machines (GBM) is a powerful ensemble learning technique used for both regression and classification problems. It builds models sequentially, each new model correcting the errors of the previous ones.
Naive Bayes is a probabilistic classifier based on Bayes' Theorem with an assumption of independence between features. It’s often used for text classification, spam detection, and other classification problems.
Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with an environment. The goal is to maximize cumulative rewards through a process of trial and error.
Time series models are used to analyze and forecast data that is collected over time, such as stock prices, weather data, or sales figures.