It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning ...

if the output values \(y\) are numbers, we call the problem a regression problem. if instead the output values are a small set of values (like ham/spam, or sunny/cloudy/rainy), then we call it a ...

Supervised learning is useful for grouping data into specific categories (classification) and understanding the relationship between variables to make predictions (regression). This type of machine ...

Supervised learning works well with labelled data, enabling tasks like classification and regression, but it requires large, high-quality datasets. In contrast, unsupervised learning identifies ...

Logistic Regression is one of the supervised machine learning algorithms which would be majorly employed for binary class classification problems where according to the occurrence of a particular ...

Training supervised models for prediction and binary classification tasks, including linear and logistic regression. This beginner-friendly course includes hands-on projects, assessments, and provides ...