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Building and Evaluating Text Classifiers in Python

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Overview

Progress from preprocessing text data to building predictive models with this practical course. You'll learn how to leverage machine learning algorithms, such as Naive Bayes and logistic regression, to classify text into categories. Using the preprocessed SMS Spam Collection dataset, the course guides you through training classifiers, making predictions, and evaluating their performance.

Syllabus

Unit 1: Training a Naive Bayes Classifier for Text Categorization

Running the Naive Bayes Classifier
Adjusting Classifier Test Size
Mastering the Naive Bayes Classifier
Crafting a Naive Bayes Classifier

Unit 2: Classifying Text with Logistic Regression in Python

Putting Logistic Regression to Work
Debugging Logistic Regression Model
Mastering Text Classification with Logistics Regression

Unit 3: Mastering Cross-Validation for Text Classification in Python

Running Cross-Validation on Text Data
Elevating Cross-Validation to 10-Folds
Fixing Cross-Validation in Naive Bayes
Implementing Cross-Validation in Python
Mastering Text Classification with Naive Bayes

Unit 4: Fine-Tuning Text Classification Models with Grid Search in Python

Optimizing Naive Bayes with Grid Search
Expanding Alpha Range in Grid Search
Debugging Grid Search Implementation
Tuning Naive Bayes with Grid Search
Mastering Grid Search in Text Classification

Unit 5: Deciphering Model Accuracy with the Confusion Matrix in NLP

Evaluating Classifier Performance
Filling in the Confusion Matrix
Mastering Confusion Matrix Evaluation

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