Data Preprocessing and Feature Engineering for Machine Learning

Data Preprocessing and Feature Engineering for Machine Learning

Data Science Festival via YouTube Direct link

Pipeline

21 of 21

21 of 21

Pipeline

Class Central Classrooms beta

YouTube videos curated by Class Central.

Classroom Contents

Data Preprocessing and Feature Engineering for Machine Learning

Automatically move to the next video in the Classroom when playback concludes

  1. 1 Intro
  2. 2 Uses of Machine Learning
  3. 3 Machine Learning Models
  4. 4 Data Format and Quality
  5. 5 Challenges of Feature Engineering
  6. 6 Open-source for Feature engineering
  7. 7 Why Open-source
  8. 8 Missing Data Imputation Techniques
  9. 9 Categorical Variables
  10. 10 Categorical Encoding Techniques
  11. 11 Encoding Techniques: Rare labels
  12. 12 Distributions
  13. 13 Mathematical transformations
  14. 14 Discretisation
  15. 15 Outliers
  16. 16 Feature Combination
  17. 17 Variable Magnitude
  18. 18 Feature scaling methods
  19. 19 Datetime Variables
  20. 20 Transactions and Time Series
  21. 21 Pipeline

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.