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Pokročilý Machine Learning v Pythone
5h 21min
Time
40
Chapters
Programovanie
Category
5.0
Rating
Expert
Level
slovak
Language
Chapters
Vitajte v kurze
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2min
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Materiály na stiahnutie
-
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Distance-based learning: Algoritmy založené na meraní vzdialeností
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7min
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Numerické atribúty a ich základné merania vzdialeností
12min
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Numerické atribúty a pokročilé merania vzdialeností
19min
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Merania vzdialeností pre kategorické atribúty
10min
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Bonusová prednáška pre kategorické atribúty
4min
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Problém zmiešaných vstupných atribútov
6min
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Štandardné riešenia pre zmiešané vstupné atribúty
8min
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Kreatívne riešenia zmiešnach vstupných atribútov
8min
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Textové atribúty
4min
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Nevybalansované dáta
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8min
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Problém nevybalansovaných dát
8min
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Vyhodnotenie modelov s nevybalansovanými dátami: Ktoré metriky použiť?
12min
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Ako môžeme vyriešiť problém nevybalansovaných dát?
12min
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"Naivné" sampling metódy
6min
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Úvod do Informovaných sampling metód
2min
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Informovaný Oversampling
8min
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Informovaný Undersampling
7min
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Ensemble modely
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3min
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Wisdom of the crowds: "Múdrosť davov"
13min
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Diverzita a kombinovanie predpovedí
11min
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Štandardné prístupy Ensemble modelov
13min
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Bagging modely
12min
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Špeciálny prípad: RandomForest Ensemble
7min
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Špeciálny prípad: ExtraTrees Ensemble
6min
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Pripomeňme si odporúčania na Feature Engineering
6min
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Isolation Forest
4min
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Boosting modely: AdaBoost
12min
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Gradient Boosting Machines
12min
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XGBoost (Extreme Gradient Boosting)
7min
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LightGBM
9min
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CatBoost
12min
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Stacking modely: Soft Voting
7min
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Dynamické budovanie Ensemble modelu: Dynamic Ensemble Building
5min
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Stacked Generalization
7min
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Deep Ensemble modely
4min
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Dynamic Ensemble modely
3min
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Snapshot Ensembling
3min
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Model Distillation
4min
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Róbert Barcík
12
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