Feature Engineering

This course presents a practical workflow for feature engineering in machine learning. It covers categorical encoding, missing-value handling, scaling, transformations, imbalanced datasets, outliers, and exploratory data analysis.

Syllabus

Feature Engineering-How to Perform One Hot Encoding for Multi Categorical Variables.
Different Types of Feature Engineering Encoding Techniques.
Why Do We Need to Perform Feature Scaling?.
How To Handle Missing Values in Categorical Features.
Featuring Engineering- Handle Categorical Features Many Categories(Count/Frequency Encoding).
Featuring Engineering- How To Handle Ordinal Categories(Ordinal Encoding).
Live-Feature Engineering-All Techniques To Handle Missing Values- Day 1.
Live-Feature Engineering-All Techniques To Handle Missing Values- Day 2.
Live-Feature Engineering-All Techniques To Handle Missing Values- Day 3.
Live-Feature Engineering-All Techniques To Handle Categorical Features - Day 4.
Summary Live Streaming-Feature Engineering- Probability Ratio Encoding- Handling Categorical Feature.
Live-Feature Engineering-All Standardization And Transformation Techniques- Day 6.
Live Discussion On Handling Imbalanced Dataset- Machine Learning.
Live Discussion On Outlier And Its Impacts On Machine Learning UseCases.
Discussing All The Types Of Feature Transformation In Machine Learning.
Step By Step Process In EDA And Feature Engineering In Data Science Projects.