Full course description
In this course, we will provide an overview to supervised learning, which is one major component of machine learning. Two most widely utilized supervised learning tasks, classification and regression, will be discussed in the course. We will explain in more detail multiple classical classification methods including the perceptron model, logistic regression, support vector machines, decision trees, K-nearest neighbor, and ensemble approaches. We will explain in detail regression models including both linear and non-linear models. This course explains how to apply these machine learning models using Python’s scikit-learn library. We are excited to embark on this journey of learning with you!
By the End of This Course, You Will be Able To:
- Utilize several classifiers and differentiate their advantages and disadvantages
- Explain and demonstrate regression analysis
- Apply ensemble learning approaches