Machine Learning Algorithms
Learn about the core machine learning tasks—regression, classification, and clustering—along with their common real-world applications and examples.
We have already learned about the three broadly classified ML techniques: supervised, unsupervised, and reinforcement learning. Let's dive in and learn about the types of ML algorithms.
Regression
Regression in machine learning analyzes how independent variables or features correlate with a dependent variable or outcome. It serves as a predictive modeling approach, where an algorithm predicts continuous outcomes.
Consider an example where we want to predict the house price using only its area. We can model this using a straight line that describes the relationship between an area (independent variable) and the price of the house (dependent variable).
However, the house price depends on various other factors, such as the number of rooms and the locality of the house. In such cases, we can use multi-regression to model the dependency of the house price on multiple independent variables.
Regression is not limited to linear relationships. We can use non-linear regressions, such as polynomial regression, logistic regression, etc, to predict the target variable.
Common regression examples
Other common examples of regression use cases include: