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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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.Start Course Now
Regression modelling Learning Outcomes
Refining a Regression Models with R
Refining a Regression Model with Python
Regression modelling Lesson Summary
Classification Modelling Learning Outcomes
Preparing Data for Classification Using R
Preparing Data for Classification Using Python
Creating a Support Vector Machine Model & Decision Forest Model
Evaluating Classification Models
Classification Modelling Learning Outcomes
Unsupervised Learning models
Unsupervised Learning models Learning Outcomes
Unsupervised Models & Building K-Means Clustering
Exploring & Creating Clustering Models with R
Exploring & Creating Clustering Models with Python
Unsupervised Learning models Lesson Summary
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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