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O tomto kurze 4min

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Nastavovanie prostredia a materiály na stiahnutie 17min

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1. Prvotný (baseline) model

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Základná intuícia prvotného modelu 5min

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Dummy classifier a Dummy regressor 9min

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Baseline model založený na heuristike 3min

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2. Evaluácia modelu

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Tréningové, zadržané a produkčné dáta 13min

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Krížová validácia (cross validation) 6min

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Obšírna intuícia 6min

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Vypočítanie CV metrík 8min

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Iterátory pre krížovú validáciu 11min

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Vyhodnotenie klasifikačného modelu 20min

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Vyhodnotenie regresného modelu 12min

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Je náš model dostatočné dobrý? 18min

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3. Prahové hodnoty a krivky

Prahové hodnoty a krivky 19min

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4. Kompozitný kód

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ColumnTransformer 8min

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Vyberanie atribútov v ColumnTransformeri 3min

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Pipeline 7min

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Komplexnejšie kompozity 11min

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5. Tuning hyperparametrov

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Hyperparameter grid 6min

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Odhadovanie času hľadania 5min

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Full search a Random search 8min

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Kompozity a tuning hyperparametrov 5min

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6. Rozhodovací strom (Decision Tree)

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Úvod do rozhodovacích stromov 10min

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Predikovanie pomocou rozhodovacieho stromu 7min

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Rozhodovacie stromy pre regresné problémy 3min

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Hyperparametre rozhodovacích stromov 11min

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7. Náhodný les (Random Forest)

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Prístup ensemble modelov 15min

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Hyperparametre náhodného lesa 10min

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Dôležitosť atribútov v náhodnom lese 7min

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8. Algoritmy založené na meraní vzdialenosti

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Intuícia modelov založených na vzdialenosti 4min

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kNN (k-Nearest Neighbors) algoritmus 11min

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Rôzne spôsoby merania vzdialenosti 8min

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rNN (Radius-nearest Neighbors) algoritmus 4min

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9. Základy nevybalansovaných dát

Nevybalansované dáta 11min

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10. Lineárna regresia

Úvod do lineárnych modelov 1min

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Uvedenie do problému 6min

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Viacnásobná lineárna regresia 4min

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Chyby lineárnej regresie 3min

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Regresné metriky 7min

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Trénovanie lineárnej regresie 5min

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Odľahlé hodnoty (outliers) 2min

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Interpretácia lineárnej regresie a ako interpretovať prienik 6min

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Interpretácia sklonu jednoduchej lineárnej regresie 3min

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Interpretácia sklonu vo viacnásobnej lineárnej regresii 3min

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Porovnávanie vplyvu vstupných premenných na výstup 4min

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Interpretácia koeficientov kategorických premenných 3min

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Interpretácia premenných v modeli so zmiešanými typmi premenných 4min

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Zhrnutie 3min

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11. Regularizované lineárne modely

Polynomiálna regresia 7min

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Kolinearita - polynomiálna regresia 6min

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Kolinearita - dummy pasca 5min

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Pretrénovanie / podtrénovanie 6min

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Regularizácia 4min

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Alpha 6min

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Regularizované modely 5min

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Ktorý regularizovaný model si vybrať 5min

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12. Logistická regresia

Úvod do logistickej regresie 1min

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Klasifikačný problém a príklady použitia 8min

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Binárna logistická regresia 2min

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Binárna logistická regresia - predpovede triedy 5min

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Polynomiálna logistická regresia 5min

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Koeficienty 9min

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Nákladová funkcia 8min

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Viactriedna klasifikácia 6min

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Príprava dát 3min

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13. Zhlukovanie

Úvod do učenia bez učiteľa 6min

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Úvod do zhlukovania 7min

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K-means 5min

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K-means tipy 14min

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DBSCAN 8min

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DBSCAN tipy 5min

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14. Redukcia dimenzionality

Úvod do redukcie dimenzionality 5min

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PCA 8min

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PCA tipy 6min

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Učenie manifoldu 7min

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15. Detekcia anomálií

Úvod do detekcie anomálií 5min

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Faktor lokálnej odľahlej hodnoty (Local Outlier Factor) 4min

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Izolujúci les (Isolation Forest) 4min

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Čo ďalej?

Diskusia a hodnotenie 1min

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Gratulujem! Čo ďalej? 2min

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Machine Learning v Pythone

Chapters