Practical ML - Model Building

Course
39 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.

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Section 5: Introduction to Linear Regression

This section will present one of the most important concepts in quantitative finance and machine learning: linear regression. Regression tasks can either be used to predict quantities (which we call regression tasks) or to predict a category (think up or down; we call these tasks classification). Understanding the key aspects of regression gives you the foundation for all of all machine learning.

Introduction to Regression Slides

01 - Introduction and Errors

02 - Regularization

03 - Bias Variance Trade-Off

04 - Use Case and Conclusion

Section 7: Logisitc Regression

In this section, we will address our case study with our first modelling algorithm: logistic regression.

Logistic Regression Slides

Logistic Regression Python Notebook

01 - Theory - Part 1

02 - Theory - Part 2

03 - Theory - Part 3

04 - Feature Engineering

05 - Data Preparation

06 - Model Training

07 - Model Evaluation

08 - Conclusion

Logistic Regression Summary Quiz

Section 8: Generalized Additive Models

In this section, we will show how to deal with one of the main weaknesses with linear models: how to address non-linearity. GAMs take the first step by using splines to better fit the data, without increasing too much complexity.

GAMs Slides

GAMs Python Notebook

01 - GAM Theory

02 - Data Preparation

03 - Model Training and Evaluation

04 - GAM Smoothing with Splines

05 - Feature Interactions and Conclusion

GAM Summary Quiz

Section 9: Decision Trees

While GAMs can do a great job addressing non-linearity, they are still limited when dealing with data that has complex relationships. Decision trees are a very powerful next step.

Decision Trees Slides

Decision Trees Python Notebook

01 - Theory - Part 1

02 - Theory - Part 2

03 - Coding Introduction

04 - Model Training and Evaluation

05 - Tree Visualization

06 - Regularization and Conclusion

Decision Trees Summary Quiz

Section 10: Random Forests

While decision tress are a huge step-up in addressing non-linearity, they do have many weaknesses. Random Forests address many of them.

Note About the Theory Section

Random Forest Python Notebook

01 - Introduction

02 - Model Building and Evaluation

03 - Model Improvement and Conclusion

Random Forest Summary Quiz