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Data Science - Regression and Clustering Models

Learn how to create regression models, data classification models, and cluster models in Azure ML, R and Python.

Publisher: Channel 9
This free online data science course will teach you about Regression and Clustering Models. You will look into what regression modelling and classification modelling are, look at their similarity, and learn how each of these models can be created in Azure ML, R, and Python. This course will also discuss the metrics for evaluating a classification model's performance. You will also examine unsupervised learning models, and more!
Data Science - Regression and Clustering Models
  • Duration

    1.5-3 Hours
  • Students

    3,476
  • Accreditation

    CPD

Description

Modules

Outcome

Certification

View course modules

Description

This course begins by discussing the process of regression modelling and how to improve the model. You will learn how to refine a regression model with R and Python as well as study cross-validation including how it can help you with your data. This course will also teach you how to use Azure ML's built-in modules sweep parameters and permutation features. You will also learn the steps needed to cross-validate a model.

You will then study the process of classification modelling and how to improve the model. Just like the regression models, you can use the Evaluate Model module and sweep parameters module in classification modelling. With this course, you will learn the metrics for evaluating a classification model's performance as well as how to create a support vector machine model and a decision forest model. You will also learn that Data Preparation (Data munging) is the most time-consuming process of a Data Science project which is an iterative process.

Expand your knowledge in data science and the use of regression, classification, and clustering models with this online course from Alison. To complete this course successfully, you need a basic knowledge of mathematics, including linear algebra and some programming experience in either R or Python. It is also advised that you first finish the 'Introduction to Data Science', 'Working with Data', and 'Visualizing Data and Exploring Models' courses. Done with them? Then start this course today!

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