Learn how machine learning algorithms work. Explore a variety of algorithms and learn how to set a structure that guides you through picking the best one for the problem at hand.
Overview
Syllabus
Introduction
- The power of algorithms in machine learning
- What you should know
- What tools you need
- Using the exercise files
- Defining model vs. algorithm
- Process overview
- Clean continuous variables
- Clean categorical variables
- Split into train, validation, and test set
- What is logistic regression?
- When should you consider using logistic regression?
- What are the key hyperparameters to consider?
- Fit a basic logistic regression model
- What is Support Vector Machine?
- When should you consider using SVM?
- What are the key hyperparameters to consider?
- Fit a basic SVM model
- What is a multi-layer perceptron?
- When should you consider using a multi-layer perceptron?
- What are the key hyperparameters to consider?
- Fit a basic multi-layer perceptron model
- What is Random Forest?
- When should you consider using Random Forest?
- What are the key hyperparameters to consider?
- Fit a basic Random Forest model
- What is boosting?
- When should you consider using boosting?
- What are the key hyperparameters to consider boosting?
- Fit a basic boosting model
- Why do you need to consider so many different models?
- Conceptual comparison of algorithms
- Final model selection and evaluation
- Next steps
Taught by
Derek Jedamski
