Linear models are a class of supervised learning models that are represented by an equation and use a linear combination of features and weights to compute the label of an unlabeled example. Linear models are simple to implement, fast to train, and relatively low in complexity. In this course, you will explore several linear models, including logistic regression, one of the most powerful linear models used in classification. Logistic regression is used to predict the probability of an outcome. While the focus of the unit will be on logistic regression, you will also be introduced to a common linear model used to solve regression problems: linear regression. You will delve into important concepts specific to the training of linear models, including the optimization algorithm, gradient descent, and the loss function evaluation tool. You will be given the opportunity to implement a logistic regression model from scratch using NumPy, and you will see a demonstration of how a linear regression model can be used to solve real-world regression problems, applying your experience to relevant scenarios.

You are required to have completed the following courses or have equivalent experience before taking this course:

• Machine Learning Foundations
• Managing Data in Machine Learning
• Training Common Machine Learning Models