multiple regression is also known as multivariable regression these methods try to model data where we have one response output variable in each observation but multiple explanatory variables in the form of vector instead of single explanatory variable the idea is to predict based on the different features present in real-world example would be extending our house prediction model to build more sophisticated model where we predict the house price based on multiple features instead of just plot area in each data sample the features could be represented in vector as plot areanumber of bedroomsnumber of bathroomstotal floorsfurnishedor unfurnished based on all these attributesthe model tries to learn the relationship between each feature vector and its corresponding house price so that it can predict them in the future polynomial regression is special case of multiple regression where the response variable is modeled as an nth degree polynomial of the input feature basically it is multiple regressionwhere each feature in the input feature vector is multiple of the model on the right in figure - to predict house prices is polynomial model of degree non-linear regression methods try to model relationships between input features and outputs based on combination of non-linear functions applied on the input features and necessary model parameters lasso regression is special form of regression that performs normal regression and generalizes the model well by performing regularization as well as feature or variable selection lasso stands for least absolute shrinkage and selection operator the norm is typically used as the regularization term in lasso regression ridge regression is another special form of regression that performs normal regression and generalizes the model by performing regularization to prevent overfitting the model typically the norm is used as the regularization term in ridge regression generalized linear models are generic frameworks that can be used to model data predicting different types of output responsesincluding continuousdiscreteand ordinal data algorithms like logistic regression are used for categorical data and ordered probit regression for ordinal data nsupervised learning supervised learning methods usually require some training data where the outcomes which we are trying to predict are already available in the form of discrete labels or continuous values howeveroften we do not have the liberty or advantage of having pre-labeled training data and we still want to extract useful insights or patterns from our data in this scenariounsupervised learning methods are extremely powerful these methods are called unsupervised because the model or algorithm tries to learn inherent latent structurespatterns and relationships from given data without any help or supervision like providing annotations in the form of labeled outputs or outcomes unsupervised learning is more concerned with trying to extract meaningful insights or information from data rather than trying to predict some outcome based on previously available supervised training data there is more uncertainty in the results of unsupervised learning but you can also gain lot of information from these models that was previously unavailable to view just by looking at the raw data often unsupervised learning could be one of the tasks involved in building huge intelligence system for examplewe could use unsupervised learning to get possible outcome labels for tweet sentiments by using the knowledge of the english vocabulary and then train supervised model on similar data points and their outcomes which we obtained previously through unsupervised learning there is no hard and fast rule with regard to using just one specific technique you can always combine multiple methods as long as they are relevant in solving the problem unsupervised learning methods can be categorized under the following broad areas of ml tasks relevant to unsupervised learning clustering dimensionality reduction anomaly detection association rule-mining