Learn about three different types of models in the course Data Science - Regression, Classification and Clustering Models: regression models, classification models and clustering models. You will also learn how each of these models can be created in Azure ML, R and Python.
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.
This free Alison course will be of great interest to learners who wish to expand their knowledge about data science and the use of regression, classification and clustering models.
Perquisites: 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'.
Having completed this course you will be able to:
- Discuss the process of regression modelling and how to improve the model;
- Describe how to refine a regression model with R;
- Explain how to refine a regression model with Python;
- Discuss the process of classification modelling and how to improve the model;
- List the metrics for evaluating a classification models performance;
- Describe how to create a support vector machine model and a decision forest model;
- Discuss the process of creating unsupervised learning models;
- Explain how to create hierarchical and k-means clustering models in R;
- Explain how to create hierarchical and k-means clustering models in Python.
All Alison courses are free to enrol, study and complete. To successfully complete this Certificate course and become an Alison Graduate, you need to achieve 80% or higher in each course assessment. Once you have completed this Certificate course, you have the option to acquire official Certification, which is a great way to share your achievement with the world. Your Alison Certification is:
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