Data Science – Regression and Clustering Models

Course Level 3: Advanced

Estimated Study Time: 2-3 hours

The course begins by introducing you to regression models. You will learn about what regression modelling is and about the steps you can take to improve your models. The course teaches you about cross-validation and how it can help you with your data. You will learn about using Azure ML’s built-in modules sweep parameters and permutation features. Next, you will learn about classification models. You can use many of same Azure ML built-in modules for classification models that you can use in regression modelling. You will also learn about the metrics for evaluating a classification model’s performance, and about creating a support vector machine model and a two-class decision forest model.

Finally, the course teaches you about unsupervised learning models. You will learn how different clustering method work and about how to evaluate cluster models. You will learn about cluster model’s K-means and hierarchical clustering. You will learn about creating clustering models in Python and R.

Prerequisites: To complete this course successfully you need a basic knowledge of mathematics, including linear algebra. Additionally, some programming experience, ideally in either R or Python, is assumed and you will need to have completed the previous courses ‘Introduction to Data Science’, ‘Data Science – Working with Data’, and ‘Data Science – Visualizing Data and Exploring Models’.

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Course Content