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Machine Learning in Python

Advanced Classification

Classification models are useful for making simple predictions, but they can also be used to perform much more sophisticated tasks. In this course, you will explore some of the more advanced classification models available in Python, including naive bayes and support vector machines.

Intermediate        11 Lessons        180 Minutes        CPD Credits
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About This Course

Build upon your existing knowledge of classification models. In this course, you will learn some advanced classification models available in Python and when they should be applied.

You will examine multinomial naive bayes, both their composition and their business applications. You will do the same for gaussian naive bayes. Finally, you will explore the utility of support vector machines.

By the end of this course, you will have an enhanced toolset for classifying and categorizing data using advanced machine learning algorithms in Python.

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Learning Outcomes

  • Understand the applications of multinomial naive bayes
  • Build a multinomial naive bayes algorithm
  • Understand the applications of gaussian naive bayes
  • Build a gaussian naive bayes algorithm
  • Understand the applications of support vector machines
  • Build a support vector machines algorithm


1. Course Introduction
2. Understanding Bayes Theorem
3. Understanding Multinomial Naive Bayes
4. Preparing Multinomial Naive Bayes Model
5. Building a Multinomial Naive Bayes Model
6. Evaluating a Multinomial Naive Bayes Model

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7. Understanding Gaussian Naive Bayes
8. Building a Gaussian Naive Bayes Model
9. Understanding Support Vector Machines
10. Configuring Support Vector Machine Parameters
11. Building a Support Vector Machine Model
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