Machine Learning

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Overview

• intro

• general staff

• playground

Topics

• Classification

• Features Transform

• Statistical classification

• Decision trees

• Linear Regression

• Non parametric regression

• Ensemble Methods

• Clustering

• DimReduction

• Manifold Learning

• anomDet

Classification
    linear
    svm
    knn
    imbalanced dataset
        undersampling
        oversampling
    
Performance Scores
    decision function
    recall 
    precision 
    f1
    Support
    Balanced Accuracy 
    roc
    auc

Statistical classification
    logistic regression
    map classifier
    naive bayes classifier

Decision trees
    impurity
    tree ensemble

Linear Regression
    linear
    polynomial
    Phase Estimation
    ridge regression
    score R2
    LS outiner - RANSAC
    Weighted least square
    Non Linear Least Squares

Non parametric regression
    kernel regression
    polynomial regression
    spline regression
    tree regression

Ensemble Methods
    bias variance tradeoff
    bagging
    random forest
    gradient boosting
    ada boost
    lightgbm
    xgboost


Clustering
    k-means
    gmm clustering
    hierarchical clustering
    density
    DBSCAN
    HDBSCAN


Dimensionality Reduction
    PCA
    oose
    SVD
    KPCA
    mds

manifold learning
    isomap
    spectral
    tsne
    umap

anaomaly detection
    zscore
    local outlier factor
    isolation_forest

pre process 
    - outlier detection
    - scaling
    - missing values
    - encoding

Feature engineering
    - feature extraction
    - feature transformation
    - feature selection
    - dimensionality reduction