Classification methods are among the most important in modern data science. Learn classification strategies and algorithms for machining learning and AI.
Machine Learning and AI Foundations: Classification Modeling
Overview
Syllabus
Introduction
- Classification problems in machine learning
- What you should know
- Defining terms
- The importance of binary classification
- Binary vs. multinomial
- So-called “black box” techniques
- One task, many algorithms
- Statistics vs. machine learning
- Model assessment vs. business evaluation
- Training and test partitions
- Lift Charts
- Gains tables
- Confusion matrix
- Overview
- Discriminant with three categories
- Discriminant with two categories
- Stepwise discriminant
- Logistic regression
- Stepwise logistic regression
- Decision Trees
- KNN
- Linear SVM
- Neural nets
- Bayesian networks
- Heterogenous ensembles
- Bagging and random forest
- Boosting and XGBoost
- Imbalanced target categories
- Interactions
- Missing data
- Bias-variance trade-off and overfitting
- Data reduction
- AutoML
- Next steps
Taught by
Keith McCormick
