Feature Engineering for Machine Learning For Free

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Ruchika oberoi

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Mar 27, 2022
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[Download] Feature Engineering for Machine Learning For Free

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What you’ll learn

  • Learn multiple techniques for missing data imputation.
  • Transform categorical variables into numbers while capturing meaningful information.
  • Learn how to deal with infrequent, rare, and unseen categories.
  • Learn how to work with skewed variables.
  • Convert numerical variables into discrete ones.
  • Remove outliers from your variables.
  • Extract useful features from dates and time variables.
  • Learn techniques used in organizations worldwide and in data competitions.
  • Increase your repertoire of techniques to preprocess data and build more powerful machine learning models.

Requirements

  • A Python installation.
  • Jupyter notebook installation.
  • Python coding skills.
  • Some experience with Numpy and Pandas.
  • Familiarity with machine learning algorithms.
  • Familiarity with Scikit-Learn.

Who this course is for:

  • Data scientists who want to learn how to preprocess datasets in order to build machine learning models.
  • Data scientists who want to learn more techniques for feature engineering for machine learning.
  • Data scientists who want to improve their coding skills and programming practices for feature engineering.
  • Software engineers, mathematicians and academics switching careers into data science.
  • Data scientists interested in experimenting with various feature engineering techniques on data competitions
  • Software engineers who want to learn how to use Scikit-learn and other open-source packages for feature engineering.


RAR password: [email protected]