ML Fundamentals problems
Regressions, trees, clustering, metrics. The classic toolkit, from scratch.
360 problems129 Easy160 Medium71 Hard
50 problems
Linear Regression
ml-001
Logistic Sigmoid
ml-002
K-Means One Iteration
ml-003
K-Nearest Neighbors
ml-004
Best Decision Tree Split
ml-005
Entropy
ml-006
Gini Impurity
ml-007
Accuracy
ml-008
Precision, Recall, F1
ml-009
K-Fold Split Indices
ml-010
2D Linear Regression via Normal Equation
ml-011
MSE Gradient
ml-012
Ridge Regression Closed Form
ml-013
Binary Cross-Entropy Loss
ml-014
Macro F1 Score
ml-015
Confusion Matrix Counts
ml-016
Mean Squared Error
ml-017
Mean Absolute Error for Regression
ml-018
Root Mean Squared Error for Regression
ml-019
R-Squared Score
ml-020
Deterministic Train/Test Split
ml-021
Shuffled Train/Test Split
ml-022
Standardization of Feature Values
ml-023
Min-Max Scaling
ml-024
Robust Scaling
ml-025
One-Hot Encoding
ml-026
Label Encoding
ml-027
KNN Regression
ml-028
Euclidean Distance
ml-029
Manhattan Distance
ml-030
Minkowski Distance
ml-031
K-Means Assignment Step
ml-032
K-Means Centroid Update
ml-033
K-Means Inertia
ml-034
Information Gain
ml-035
Majority Class
ml-036
Gaussian Naive Bayes Likelihood
ml-037
Naive Bayes Predict
ml-038
Bootstrap Sample
ml-039
Bagging Feature Subset
ml-040
PCA Mean Centering
ml-041
Covariance Matrix
ml-042
Power Iteration
ml-043
Projection onto a Principal Component
ml-044
Explained Variance Ratio
ml-045
Cosine Similarity of Feature Vectors
ml-046
Z-Score Outlier Detection
ml-047
Inverse-Frequency Class Weights
ml-048
Stratified Train/Test Split
ml-049
Bootstrap Confidence Interval
ml-050
Showing 50 of 360 problems.