Language Classification with Naive Bayes in Python

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In this Guided Project, you will:

H​ow to clean and preprocess data for language classification

H​ow to train and assess a Multinomial Naive Bayes Model

H​ow to use subword units to counteract the effects of class imbalance in language classification

Clock60-75 minutes
IntermediateIntermediate
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

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.

Skills you will develop

StatisticsMachine LearningNatural Language Processing

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Exploratory data analysis of raw data, as well as some basic visualization

  2. Data cleaning and preprocessing relevant for task

  3. Theory behind and training of a Multinomial Naive Bayes Model

  4. M​aking adjustments to model to take into account class imbalance using theory behind Naive Bayes

  5. U​sing subword units to further counteract class imbalance and improve model performance

How Guided Projects work

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

Instructor

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