Go beneath the surface of classification algorithms and metrics, implementing them from scratch for deeper understanding. Bypass commonly-used libraries such as scikit-learn to construct Logistic Regression, k-Nearest Neighbors, Naive Bayes Classifier, and Decision Trees from ground up. This course includes creating the AUCROC metric for Logistic Regression, among others.
Overview
Syllabus
- Lesson 1: Understanding the Confusion Matrix, Precision, and Recall in Classification Metrics
- Lesson 2: Implementing and Interpreting AUCROC for Logistic Regression Models
- Lesson 3: Implementing k-Nearest Neighbors Algorithm in Python
- Lesson 4: Implementing the Naive Bayes Classifier from Scratch in Python
- Lesson 5: Understanding and Implementing Decision Tree Splits
- Lesson 6: Building a Decision Tree from Scratch in Python
