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Machine Learning in Python

Feature Engineering

Not all datasets are fully optimized for the machine learning model at hand; feature engineering allows you to correct this. In this course, you will explore the 3 types of feature engineering: feature scaling, feature selection and dimensionality reduction.

Advanced        8 Lessons        150 Minutes        CPD Credits
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About This Course

Ensure your data is in the most optimal shape to fit your machine learning model. In this course, you will examine the concept of feature engineering and how it can lead to better, more accurate performance.

You will scale data features into uniform units. You will experiment with the chi-square test to select features. You will also reduce features by performing principal component analysis.

By the end of this course, you will have a powerful tool for manipulating and transforming your data into a form which ensures optimal performance from your machine learning algorithms.

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Learning Outcomes

  • Scale data features into uniform units
  • Select features based on Chi Square test
  • Reduce features using Principal Component Analysis


1. Course Introduction
2. The Value of Feature Scaling
3. Feature Scaling with Min and Max Values
4. Standardization and Robust Scaling
5. Value of Feature Selection
6. Applying Feature Selection

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7. Dimensionality Reduction
8. Principal Component Analysis
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