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Machine Learning in Python

Advanced Regression

A more robust predictive model will naturally result in more granular, accurate predictions. In this course, you will explore some of the more advanced regression models, from nonparametric forms like decision tree and k-nearest neighbors regression to support vector and logistic regression.

Advanced        8 Lessons        90 Minutes        CPD Credits
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About This Course

Building on your existing knowledge of regression analysis, in this course, you will explore some much more technically complex regression models in Python.

You will examine the difference between parametric and nonparametric regression and when the latter can be useful. You will learn how to train decision tree, k-nearest neighbor and support vector regression models. Finally, you will explore the applications of logistic regression.

By the end of this course, you will have a deep understanding of regression analysis and an appreciation of its utility in the world of business. You should be confident identifying when and where each model would be appropriate to deploy in your own analysis.

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Learning Outcomes

  • Understand the difference between parametric and nonparametric regression
  • Understand when nonparametric regression can be useful
  • Learn how to train decision tree, k nearest neighbor, and support vector regression
  • Understand the applications of logistic regression


1. Course Introduction
2. Understanding Nonparametric Regression
3. Applying Decision Tree Regression
4. Applying K-nearest Neighbors Regression
5. Applying Support Vector Regression
6. Understanding Logistic Regression

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7. Checking Assumptions of Logistic Regression
8. Applying Logistic Regression
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