Practical Machine Learning in Finance
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Discussions
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
Products
Course
Section
Lesson
Introduction to Feature Engineering Sumary Quiz
Introduction to Feature Engineering Sumary Quiz
Practical Machine Learning in Finance
Buy now
Learn more
Discussions
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
10 questions
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