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

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 ...

Learning Outcomes Express why Statistical Learning is important and how it can be used. Identify the strengths, weaknesses and caveats of different models and choose the most appropriate model for a ...

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 ...