Feature selection, Modelling selection and tuning

Machine learning teaches computers to do what comes naturally to humans and animals: learn from experience. Machine learning algorithms use computational methods to “learn” information directly from data without relying on a predetermined equation as a model.

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About the course

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Data modeling is an essential part of the data science pipeline. This, combined with the fact that it is a very rewarding process, makes it the one that often receives the most attention among data science learners. However, things are not as simple as they may seem, since there is much more to it than applying a function from a particular class of a package and applying it on the data available.

A big part of data science modeling involves evaluating a model, for example, making sure that it is robust and therefore reliable. Also, data science modeling is closely linked to creating an information rich feature set. Moreover, it entails a variety of other processes that ensure that the data at hand is harnessed as much as possible.

In order for a model to maximize its potential, it needs an information rich set of features. The latter can be created in various ways. Whatever the case, cleaning up the data is a prerequisite. This involves removing or correcting problematic data points, filling in missing values wherever possible, and in some cases removing noisy variables.

Skills you will gain

  • Data pre-processing
  • Model selection
  • Feature extraction
  • Model evaluation
  • Text classification
  • Parameters Tuning

Course Syllabus

Module 1

Feature selection, Modelling selection and tuning

4.0 Hrs

1 Quiz
  • Hands on exercise for feature engineering
  • Feature engineering lab exercise using different algorithms
  • How to tune the models or improve performance
  • Concept of upsampling and downsampling
  • Hands on exercise showing tuning of model
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Course certificate

Get Feature selection, Modelling selection and tuning course completion certificate from Great learning which you can share in the Certifications section of your LinkedIn profile, on printed resumes, CVs, or other documents.