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Course

Feature Engineering for Machine Learning in Python

Intermediate

4.7+

Updated 05/2025

Create new features to improve the performance of your Machine Learning models.

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PythonMachine Learning4 hours16 videos53 Exercises4,350 XP33,427Statement of Accomplishment

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Course Description

Every day you read about the amazing breakthroughs in how the newest applications of machine learning are changing the world. Often this reporting glosses over the fact that a huge amount of data munging and feature engineering must be done before any of these fancy models can be used. In this course, you will learn how to do just that. You will work with Stack Overflow Developers survey, and historic US presidential inauguration addresses, to understand how best to preprocess and engineer features from categorical, continuous, and unstructured data. This course will give you hands-on experience on how to prepare any data for your own machine learning models.

Prerequisites

Supervised Learning with scikit-learn

1

Creating Features

Why generate features?

Getting to know your data

Selecting specific data types

Dealing with categorical features

One-hot encoding and dummy variables

Dealing with uncommon categories

Numeric variables

Binarizing columns

Binning values

2

Dealing with Messy Data

Why do missing values exist?

How sparse is my data?

Finding the missing values

Dealing with missing values (I)

Listwise deletion

Replacing missing values with constants

Dealing with missing values (II)

Filling continuous missing values

Imputing values in predictive models

Dealing with other data issues

Dealing with stray characters (I)

Dealing with stray characters (II)

Method chaining

3

Conforming to Statistical Assumptions

Data distributions

What does your data look like? (I)

What does your data look like? (II)

When don't you have to transform your data?

Scaling and transformations

Normalization

Standardization

Log transformation

When can you use normalization?

Removing outliers

Percentage based outlier removal

Statistical outlier removal

Scaling and transforming new data

Train and testing transformations (I)

Train and testing transformations (II)

4

Dealing with Text Data

Encoding text

Cleaning up your text

High level text features

Word counts

Counting words (I)

Counting words (II)

Limiting your features

Text to DataFrame

Term frequency-inverse document frequency

Inspecting Tf-idf values

Transforming unseen data

Using longer n-grams

Finding the most common words

Feature Engineering for Machine Learning in Python

Course
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Included withPremium or Teams

Don’t just take our word for it

*4.7

from 210 reviews

81%

16%

2%

0%

0%

Highest to Lowest
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Most recent
Top reviews

Christian

about 17 hours

Ridwan

about 20 hours

AKHIRANAND

3 days

Yarly

3 days

It's a great introduction to feature engineering!

Samuel

4 days

This course provided a solid foundation in one of the most critical aspects of machine learning—feature engineering. The explanations were clear, and the hands-on examples helped reinforce the concepts. I especially appreciated the focus on practical techniques like encoding, scaling, and handling missing data. A great resource for anyone looking to strengthen their ML pipeline skills!

Shahad

4 days

Christian

Ridwan

AKHIRANAND

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