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CodeSignal

Classification Algorithms and Metrics

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Overview

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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.

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

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