AI Theory and CodingClassification Algorithms and Metrics

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.

Calculating True Negatives and False Positives in Medical Diagnostics

Precision and Recall in Medical Diagnostics

Precision Calculation in Medical Diagnostics

Medical Test Recall Calculation Correction

Calculating Precision and Recall in Medical Diagnostics

Evaluating the Diagnostic Test with AUC-ROC

Fine-Tuning Thresholds for ROC Curve Analysis

Diagnostic Test AUCROC Calculation Correction

Calculating the AUC-ROC Metric

Plotting the ROC curve

Adjusting k in k-Nearest Neighbors Classifier

Manhattan Distance in k-NN Classifier

Fruit Ripeness Classification Conundrum

Classifying Fruit Ripeness with k-NN

Navigating the Cosmos: Implementing k-NN Majority Vote

Predicting the Weather with Naive Bayes

Forecast Predictor: Calculating Prior Probabilities

Calculating Normalized Values in Weather Data Analysis

Laplace Smoothing in the Naive Bayes Classifier

Predict the Play Day with Naive Bayes

Exploring Gini Index in Movies Dataset

Calculating the Weighted Gini Index

Debugging Gini Index Calculation in Movie Recommendations

Implementing the Dataset Split Function

Calculating the Gini Index for a Movie Recommendation System

Building and Visualizing a Decision Tree from Scratch

Creating Terminal Nodes for Decision Trees

Recursive Tree Splitting Challenge