Data Science - Regression and Clustering Models - Revised
Learn how to create regression models, classification models and clustering models in Azure ML, R and Python.
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With this online course, Data science and Machine Learning essentials building Machine Learning Models, you will learn about three different types of models regression, classification and clustering. You will learn about how each of these models can be created in Azure ML, R and Python.
The course with reintroducing you to regression, where you will learn about what regression modelling is. You will learn about the steps you can take to improve your models. You will learn about Cross-validation is and how it can help you with your data. You will learn about using Azure MLs built-in modules Sweep parameters, and Permutation feature importance.
In the second module, you will learn about classification modelling, how the process is similar to the same as regression. You will learn you can uses many of same Azure ML built-in modules in classification that you could use in regression modelling. You will learn about the metrics for evaluating a classification models performance. You
The last module will teach you about unsupervised learning models. You will learn about cluster models K-means and hierarchical clustering. You will learn about how each clustering method works and about how to evaluate cluster models. You will learn about creating a clustering models in Python and R.
This free Alison course would be of great interest to those who wish to learn and expand their knowledge about data Science.
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 course Data Science and Machine Learning Essentials Introduction to Data Science, Working with Data and Visualizing data and Exploring ModelsStart Course Now
Upon successful completion of this course the learner will be able to:
- Discuss the process of regression modelling and how to improve the model
- Identify how to refine a regression model with R
- Identify how to refine a regression model with Python
- Discuss the process of classification modelling and how to improve the model
- Recognise the metrics for evaluating a classification models performance
- Outline how to create a support vector machine model and a decision forest model
- Discuss the process of creating unsupervised learning models
- Recognise how to create hierarchical and k-means clustering models in R
- Recognise how to create hierarchical and k-means clustering models in Python
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