Wrap-up

Feature Engineering per il Machine Learning in Python

Robert O'Callaghan

Director of Data Science, Ordergroove

Chapter 1

  • How to understand your data types
  • Efficient encoding or categorical features
  • Different ways to work with continuous variables
Feature Engineering per il Machine Learning in Python

Chapter 2

  • How to locate gaps in your data
  • Best practices in dealing with the incomplete rows
  • Methods to find and deal with unwanted characters
Feature Engineering per il Machine Learning in Python

Chapter 3

  • How to observe your data's distribution
  • Why and how to modify this distribution
  • Best practices of finding outliers and their removal
Feature Engineering per il Machine Learning in Python

Chapter 4

  • The foundations of word embeddings
  • Usage of Term Frequency Inverse Document Frequency (Tf-idf)
  • N-grams and its advantages over bag of words
Feature Engineering per il Machine Learning in Python

Next steps

  • Kaggle competitions
  • More DataCamp courses
  • Your own project
Feature Engineering per il Machine Learning in Python

Thank You!

Feature Engineering per il Machine Learning in Python

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