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.
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
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
Your workspace is a cloud desktop right in your browser, no download required
In a split-screen video, your instructor guides you step-by-step
Excellent and well organized course. Instructor explained concepts clearly and encouraged us to attempt the steps before he showed his solution.
Excellent course, recommended for a starter in NLP. The teaching pace and topics covered are just right. Kudos!
Thank you, required more information on naive Bayes with classification.
Nice explanation by the instructor throughout the project :)
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You'll learn by doing through completing tasks in a split-screen environment directly in your browser. On the left side of the screen, you'll complete the task in your workspace. On the right side of the screen, you'll watch an instructor walk you through the project, step-by-step.
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