Course 4 : Feature Engineering (Selection/Extraction)

Categories Advanced AI

About Course

The goal of feature engineering is to extract relevant information from the data, uncover hidden patterns, and represent the data in a way that is more suitable for the machine learning algorithms to learn from. It is a critical step in the data preprocessing pipeline and can have a significant impact on the model's predictive power.

Course Curriculum

Basic data transformation methods

Handling Missing Data

00:00
Categorical Variable Encoding

00:00
Feature Scaling and Normalization

00:00
Feature Interaction and Polynomial Features

00:00
Time-Based Features:

00:00
Domain-Specific Feature Engineering

00:00
Handling Outliers:

00:00

Feature Selection methods

Feature Extraction methods

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Course 4 : Feature Engineering (Selection/Extraction)

About Course

What I will learn?

Course Curriculum

Basic data transformation methods

Handling Missing Data

Categorical Variable Encoding

Feature Scaling and Normalization

Feature Interaction and Polynomial Features

Time-Based Features:

Domain-Specific Feature Engineering

Handling Outliers:

Feature Selection methods

Feature Relevance and Redundancy

Filter Methods:

Wrapper Methods:

Embedded Methods:

Tree-based Methods:

Univariate Feature Selection:

Feature Extraction methods

Feature Extraction vs. Feature Selection:

Handling Multicollinearity:

Dimensionality Reduction Techniques: PCA

Dimensionality Reduction Techniques: SVD

Dimensionality Reduction Techniques: LDA

Dimensionality Reduction Techniques: NMF

Material Includes

Target Audience