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