Basic Terminologies of ML

#machinelearning #artificialintelligence #aicontent Algorithm: A set of rules or procedures used to solve a particular problem or perform a specific task. Training set: A set of data used to train a machine learning model. The training set is used to teach the model how to make predictions or classify new data. Test set: A set of data used to test the performance of a machine learning model. The test set is used to evaluate how well the model performs on new data. Features: The inputs to a machine learning model. Features can be numerical, categorical, or textual data. Labels: The outputs or predictions of a machine learning model. Labels can be numerical, categorical, or textual data. Supervised learning: A type of machine learning where the model is trained using labeled data. Unsupervised learning: A type of machine learning where the model is trained using unlabeled data. Regression: A type of supervised learning where the goal is to predict a continuous numerical value. Classification: A type of supervised learning where the goal is to predict a categorical value. Clustering: A type of unsupervised learning where the goal is to group similar data points together. Neural network: A type of machine learning model inspired by the structure of the human brain. Neural networks are composed of layers of interconnected nodes (or neurons) that can learn to recognize patterns in data. Overfitting: When a machine learning model becomes too complex and starts to memorize the training set instead of generalizing to new data. Underfitting: When a machine learning model is too simple and fails to capture the underlying patterns in the data. Bias: When a machine learning model is systematically wrong in its predictions. Variance: When a machine learning model is too sensitive to the noise in the data and produces inconsistent predictions.