skill PathFeature Engineering

The key to building high-quality, reliable machine learning models is feeding that model high-quality data. Feature Engineering is an essential–though sometimes overlooked–step in ensuring the quality of your data. Feature Engineering is the process of taking raw data, scaling it, normalizing it, and transforming it so it can be used by your machine learning model. It’s the process of turning your attributes into features, and ensuring that the data you are using meets the assumption of the model you are training.

40 hours

Includes

Python, Machine Learning, Algorithms, Datasets, Feature Selection, Dimensionality Reduction, Engineering, Data Transformation, and more...

Experience

Intermediate

For those who want to

  • Learn how machine learning engineers transform datasets into informative, actionable data
  • Gain knowledge needed to pivot into Machine Learning engineering roles
  • Understand real-world applications for lofty machine learning concepts

Earn

Certificate of Completion

What will you be able to do?

Here are just a few things you’ll be able to do with this skill.

What will you be able to do?

Here are just a few things you’ll be able to do with this skill.

DIMENSIONALITY REDUCTION

Reduce dimensionality with unsupervised learning methods that make complex, raw data easier to analyze.

FEATURE SELECTION

Extract features from datasets to summarize data so that it is informative and non-redundant.

IMPLEMENT METHODS

Identify different feature selection methods and know best practices for implementing the methods in real-world applications.

You really feel like there's nothing you can't learn, which in turn builds so much confidence in your skills and gives the momentum to keep learning.

Joey
Codecademy Learner

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