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Python Academy · Lesson

Feature Engineering and Selection

Discover techniques to enhance the predictive power of your models.

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Feature Engineering and Selection

Feature engineering and selection are crucial steps in building effective machine learning models. These processes involve creating new features and selecting the most relevant ones to improve model performance.

In this lesson, you’ll learn techniques for feature engineering and selection to enhance predictive accuracy.

Feature Engineering and Selection — illustration 1

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What is Feature Engineering?

Feature engineering involves creating new features from raw data to make machine learning models more effective. Examples include:

  • Extracting time-based features like day, month, or hour from timestamps.
  • Combining multiple features into a new feature.
  • Scaling numerical features to standardize their range.

All lessons in this course

  1. Introduction to Machine Learning
  2. Supervised Learning with Scikit-Learn
  3. Unsupervised Learning
  4. Feature Engineering and Selection
  5. Introduction to Neural Networks
  6. Introduction to TensorFlow and Keras
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