Net Course
Dindi sa bopp ci yoonu tek ci Net Course bi, ñu ko defar ngir jàngalekat yu bëgg a xam xam-xamu tënk mbind ak seetaan njurum-data. Dugal ci njàngal, xamle, ak yëngu-yëngal model ak jëfe ci loxo ci cross-validation, tànn mbir yi, ak hyperparameter tuning. Gënal sa xam-xam ci njurum-data, xeeñu mbir yi, ak model yu Machine Learning niki Naive Bayes ak SVM. Am xam-xam ci wàllu jëf yu yoon ci njurum-data ak mbir yu leer, ngir sa bopp àgg ci jafe-jafe yu dëgg-dëgg.

flexible workload from 4 to 360h
certificate recognized by MEC
What will I learn?
Dindi sa bopp ci yoonu tek ci Net Course bi, ñu ko defar ngir jàngalekat yu bëgg a xam xam-xamu tënk mbind ak seetaan njurum-data. Dugal ci njàngal, xamle, ak yëngu-yëngal model ak jëfe ci loxo ci cross-validation, tànn mbir yi, ak hyperparameter tuning. Gënal sa xam-xam ci njurum-data, xeeñu mbir yi, ak model yu Machine Learning niki Naive Bayes ak SVM. Am xam-xam ci wàllu jëf yu yoon ci njurum-data ak mbir yu leer, ngir sa bopp àgg ci jafe-jafe yu dëgg-dëgg.
Elevify advantages
Develop skills
- Xam njurum-data: Tënk, yeesal, ak sëtël mbind mi ci barke.
- Rafetal model yi: Defar tànn mbir yi ak hyperparameter tuning.
- Seetaan doxal gi: Jëfandiko accuracy, precision, recall, ak F1-score.
- Xeeñu mbir yi: Jëfandiko TF-IDF, Word2Vec, ak Bag of Words.
- Tabax classificateur yi: Defar Naive Bayes, SVM, ak model yu neural network.
Suggested summary
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