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Course 4 : Feature Engineering (Selection/Extraction)

About Course

The goal of feature engineering is to extract relevant information from the data, uncover hidden patterns, and represent the data in a way that is more suitable for the machine learning algorithms to learn from. It is a critical step in the data preprocessing pipeline and can have a significant impact on the model's predictive power.
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What I will learn?

  • Data transformations in pre-processing step
  • Feature selection methods
  • Feature Extraction methds

Course Curriculum

Basic data transformation methods

  • Handling Missing Data
  • Categorical Variable Encoding
  • Feature Scaling and Normalization
  • Feature Interaction and Polynomial Features
  • Time-Based Features:
  • Domain-Specific Feature Engineering
  • Handling Outliers:

Feature Selection methods

Feature Extraction methods

Student Ratings & Reviews

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Material Includes

  • Self learning videos
  • PPTs/PDFs
  • Python notebook files
  • Datasets
  • Quiz

Target Audience

  • Data Scientists
  • ML practitioners
  • Data Engineers