Introduction to Feature Engineering

Introduction to Feature Engineering

Feature Engineering 101 & 102

Unlock the Full Potential of Your Machine Learning Models

Feature Engineering is the secret weapon behind high-performing machine learning models. "Introduction to Feature Engineering" is a structured, technique-driven course designed to teach you the most essential feature engineering methods used in the industry.

Each lesson in this course introduces a new feature engineering technique, explaining its theoretical foundation and demonstrating its application on a never-before-seen loan default prediction dataset—created exclusively for this course. Whether you're a beginner or an experienced data scientist, this course will help you transform raw data into powerful predictive features.

Course Overview

The course is divided into two parts:

• Feature Engineering 101: Covers fundamental techniques like handling missing values, outlier detection, scaling, transformations, and encoding.

• Feature Engineering 102: Focuses on advanced techniques, including time-based features, handling imbalanced datasets, aggregations, and feature selection.

Each lesson is focused on a single technique, ensuring clear, practical, and in-depth learning.

What You Will Learn

Feature Engineering 101: Foundations

• Introduction to the Course: Understanding the importance of feature engineering in machine learning.

• Dataset Analysis (Parts 1 & 2): Exploring dataset structure, patterns, and initial cleaning.

• Handling Missing Values (Parts 1-3): Applying various imputation techniques to deal with missing data.

• Outlier Detection and Removal (IQR Method): Identifying and handling extreme values.

• Feature Transformations – Scaling Features: Normalization, Standardization, and Robust Scaling techniques.

• Feature Transformations – Mathematical Transformations: Applying log, polynomial, and other transformations.

• Advanced Transformations: More complex feature transformations for better predictive power.

• Encoding Categorical Features: One-hot encoding, ordinal encoding, and target encoding.

• Handling High Cardinality in Features: Managing categorical variables with a large number of unique values.

Feature Engineering 102: Advanced Techniques

• Temporal Features (Parts 1 & 2): Extracting and utilizing time-based insights.

• Aggregating Features: Creating summary statistics and aggregations for improved performance.

• Handling Imbalanced Datasets (Parts 1 & 2): Techniques like SMOTE, undersampling, and oversampling.

• Encoding High Cardinality Features: Specialized techniques for encoding categorical variables with many unique values.

• Feature Selection Techniques: Filter, wrapper, and embedded methods for selecting the most impactful features.

Why Take This Course?

✔ Structured, Technique-Focused Learning – Each video introduces a single, well-defined technique.
✔ Real-World Application – Every method is demonstrated on a custom-built loan default prediction dataset.
✔ Comprehensive Coverage – Covers both basic and advanced feature engineering methods.
✔ No Unnecessary Theory – Focuses on techniques that directly impact machine learning performance.
✔ Industry-Relevant Skills – Learn practical methods used by data scientists and machine learning engineers.

Master Feature Engineering Today!

Feature Engineering is a critical skill for anyone working with machine learning models.

Enroll in "Introduction to Feature Engineering" today and start transforming raw data into high-impact features!