• $59

Practical ML - Feature Engineering

  • Closed
  • Course
  • 27 Lessons

Practical Machine Learning in Finance is a hands-on course that teaches you how to apply machine learning to real investment problems. You’ll learn by doing—building classification models, engineering features, handling class imbalance, evaluating performance, and avoiding common pitfalls. Designed for finance professionals and students, the course focuses on practical implementation, not just theory.

Contents

Section 2: Introduction to Feature Engineering

In this section, we will cover one of the most important (if not THE most important) part of machine learning: creating good features. Creating features is not just science - it is also an art. This section will teach some basic concepts about how to create features, and then go through the process of creating the features used throughout the course.

Introduction to Feature Engineering Slides
Introduction to Feature Engineering Python Notebook
01 - Theory - Part 1
Preview
02 - Theory - Part 2
03 - Theory - Part 3
04 - Coding: Introduction
05 - Coding: Resampling
06 - Coding: Dealing with Missing Values
07 - Coding: Outliers
08 - Coding: Transformations
09 - Coding: Lookahead Bias
10 - Coding: Dummy Variables
11 - Coding: Time Awareness
12 - Coding: Interaction Terms
Preview
13 - Coding: Basic Feature Selection
Introduction to Feature Engineering Sumary Quiz

Section 4: Case Study - Feature Engineering

In this section, we will apply what we learned in the Introduction to Feature Engineering section and design the features we will use in this case study: Predicting if the S&P 500 will go up or down next month.

Feature Engineering Slides
Feature Engineering Python Notebook
01 - Imports
02 - Features 1 to 3
03 - Features 4 to 6
04 - EDA, Part 1
05 - EDA, Part 2
06 - EDA, Part 3
07 - EDA, Part 4
08 - EDA, Part 5
Feature Engineering Summary Quiz