Language Classification with Naive Bayes in Python
How to clean and preprocess data for language classification
How to train and assess a Multinomial Naive Bayes Model
How to use subword units to counteract the effects of class imbalance in language classification
In this 1-hour long project, you will learn how to clean and preprocess data for language classification. You will learn some theory behind Naive Bayes Modeling, and the impact that class imbalance of training data has on classification performance. You will learn how to use subword units to further mitigate the negative effects of class imbalance, and build an even better model.
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Будете учиться пошагово
На видео, которое откроется рядом с рабочей областью, преподаватель объяснит эти шаги:
Exploratory data analysis of raw data, as well as some basic visualization
Data cleaning and preprocessing relevant for task
Theory behind and training of a Multinomial Naive Bayes Model
Making adjustments to model to take into account class imbalance using theory behind Naive Bayes
Using subword units to further counteract class imbalance and improve model performance
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