These codes are used in the machine learning online course provided by www.youtube.com/@meanxai.
- [MXML-1-01] Basic algorighm for KNN classification
- [MXML-1-02] Optimal K value and decision boundary
- [MXML-1-03] The curse of dimensionality and lazy learner
- [MXML-1-04] Weighted KNN (WKNN)
- [MXML-1-05] Handling categorical data: Matching coefficient and Jaccard coefficient
- [MXML-1-06] Handling categorical data: Inverse Occurrence Frequency (IOF) similarity
- [MXML-1-07] KNN regression algorithm
- [MXML-2-01] Introduction to Decision Tree
- [MXML-2-02] ID3/C4.5 Classification (1) - IG, Split finding
- [MXML-2-03] ID3/C4.5 Classification (2) - IGR, Pruning
- [MXML-2-04] ID3/C4.5 Regression - SDR, CV
- [MXML-2-05] CART Classification (1) - Overview
- [MXML-2-06] CART Classification (2) - IG, Split finding
- [MXML-2-07] CART Classification (3) - Implement CART classification from scratch
- [MXML-2-08] CART Classification (4) - Feature importance
- [MXML-2-09] CART Classification (5) - Cost Complexity Pruning (CCP)
- [MXML-2-10] CART Classification (6) - Implement CCP
- [MXML-2-11] CART Regression - Implement CART regression from scratch
- [MXML-3-01] Ordinary Least Square (OLS) (1) - Basic algorithm
- [MXML-3-02] Ordinary Least Square (OLS) (2) - Regularization, Ridge, Lasso
- [MXML-3-03] Ordinary Least Square (OLS) (3) - Mean-Centering, Normalization
- [MXML-3-04] Total Least Square (TLS) - Algorithm and implementation
- [MXML-3-05] Locally Weighted Regression (WLR) - Algorithm and implementation
- [MXML-3-06] Random Sample Consensus (RANSAC) (1) - Basic algorithm
- [MXML-3-07] Random Sample Consensus (RANSAC) (2) - Implement RANSAC from scratch
- [MXML-4-01] Binary classification (1) - Basic algorithm
- [MXML-4-02] Binary classification (2) - Implementation
- [MXML-4-03] Multiclass classification in Logistic Regression (1) - One-vs-Rest
- [MXML-4-04] Multiclass classification in Logistic Regression (2) - Softmax Regression
- [MXML-4-05] Locally Weighted Logistic Regression (LWLR)
- [MXML-5-01] Constrained LP and QP problem
- [MXML-5-02] Lagrange Method for EQP and IQP
- [MXML-5-03] Lagrange Dual Method for EQP and IQP
- [MXML-5-04] KKT Method for EQP and IQP
- [MXML-6-01] Linear Hard Margin (1) - Basic algorithm
- [MXML-6-02] Linear Hard Margin (2) - Implement this from scratch
- [MXML-6-03] Linear Soft Margin (1) - Basic algorithm
- [MXML-6-04] Linear Soft Margin (2) - Implement this from scratch
- [MXML-6-05] Non-linear SVM (1) - Kernel trick, Kernel function
- [MXML-6-06] Non-linear SVM (2) - Implement nonlinear SVM from scratch
- [MXML-6-07] Multiclass classification in SVM (1) - One-vs-One (OvO)
- [MXML-6-08] Multiclass classification in SVM (2) - One-vs-Rest (OvR)
- [MXML-6-09] Support Vector Linear Regression - Implement this from scratch
- [MXML-6-10] Support Vector Non-linear Regression - Implement this from scratch
- [MXML-7-01] Objective function, Local minimum problem
- [MXML-7-02] Implement K-Means clustering from scratch
- [MXML-7-03] K-Means++ algorithm, implementation from scratch
- [MXML-7-04] How to find the optimal number of clusters - Elbow, Silhouette
- [MXML-8-01] Basic algorithm for Random Forest
- [MXML-8-02] Implement Random Forest from scratch
- [MXML-8-03] Out-Of-Bag (OOB) score
- [MXML-8-04] Proximity matrix and Handling missing values
- [MXML-8-05] Implement missing value imputation from scratch
- [MXML-8-06] Proximity matrix and Outlier detection
- [MXML-8-07] Isolation Forest
- [MXML-9-01] Basic algorithm for AdaBoost
- [MXML-9-02] Implement AdaBoost from scratch
- [MXML-9-03] Multiclass classification in AdaBoost - SAMME Algorithm
- [MXML-9-04] AdaBoost Regression - Implement this from scratch
- [MXML-10-01] GBM regression algorithm - Training and prediction process
- [MXML-10-02] GBM regression algorithm details
- [MXML-10-03] Implement GBM/SGBM regression from scratch
- [MXML-10-04] GBM classification algorithm - Training and prediction process
- [MXML-10-05] GBM classification algorithm details
- [MXML-10-06] Implement GBM/SGBM classification from scratch
- [MXML-10-07] Multiclass classification in GBM
- [MXML-11-01] Exact Greedy Algorithm for regression
- [MXML-11-02] XGBoost regression algorithm details
- [MXML-11-03] Implement XGBoost regression from scratch
- [MXML-11-04] Exact Greedy Algorithm for classification
- [MXML-11-05] XGBoost classification algorithm details
- [MXML-11-06] Implement XGBoost classification from scratch
- [MXML-11-07] Approximate Algorithm - Global, Local variant
- [MXML-11-08] Weighted Quantile Sketch
- [MXML-11-09] Sparsity-aware Algorithm
- [MXML-12-01] Histogram-based split finding
- [MXML-12-02] Gradient-based One-Side Sampling (GOSS)
- [MXML-12-03] Greedy Feature Bundling Algorithm
- [MXML-12-04] Merge Exclusive Features Algorithm
- [MXML-12-05] Implement EFB algorithm from scratch