Practical Machine Learning in Finance
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Onboarding and Orientation

Getting Started
Resources

ML Pathways Overview
Section 1: Introduction to Machine Learning

Introduction to ML Slides
Introduction to Machine Learning
Section 2: Introduction to Feature Engineering

Introduction to Feature Engineering Slides
Introduction to Feature Engineering Python Notebook
01 - Theory - Part 1
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
13 - Coding: Basic Feature Selection
Introduction to Feature Engineering Sumary Quiz
Section 3: Case Study Overview

Case Study Overview Slides
Case Study Overview
Section 4: Case Study - Feature Engineering

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
Section 5: Introduction to Linear Regression

Introduction to Regression Slides
01 - Introduction and Errors
02 - Regularization
03 - Bias Variance Trade-Off
04 - Use Case and Conclusion
Section 6: Understanding Classification Reports: The Confusion Matrix

Understanding the Confusion Matrix Slides
01 - Understanding the Confusion Matrix
Section 7: Logisitc 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

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

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

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
Section 11: Support Vector Machines

SVM Slides
SVM Python Notebook
01 - Theory - Part 1
02 - Theory - Part 2
03 - Theory - Part 3
01 - Coding: Introduction
02 - Coding: Model Building and Evaluation
03 - Coding: Model Visualization
SVM Summary Quiz
Section 12: Deep Learning (Neural Networks)

Deep Learning Slides
Deep Learning Python Notebook
01 - Theory - Part 1
02 - Theory - Part 2
03 - Theory - Part 3
04 - Coding: Introduction
05 - Coding: Initialization and Model Building
06 - Coding: Model Evaluation
Deep Learning Summary Quiz
Section 13: Hierarchical Clustering

Clustering Slides
Hierarchical Clustering Python Notebook
01 - Theory
02 - Coding Introduction
03 - Initialization
04 - Feature Engineering
05 - Clustering
Hierarchical Clustering Summary Quiz

Section 7: Logisitc Regression

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

11 Lessons