Data Science - Regression and Clustering Models - Revised
Learn how to create regression models, data classification models, and cluster models in Azure ML, R and Python.
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CertificationView course modules
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!
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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