Introduction to Natural Language ProcessingBuilding and Evaluating Text Classifiers in Python

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.

Running the Naive Bayes Classifier

Adjusting Classifier Test Size

Mastering the Naive Bayes Classifier

Crafting a Naive Bayes Classifier

Putting Logistic Regression to Work

Debugging Logistic Regression Model

Mastering Text Classification with Logistics Regression

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

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

Evaluating Classifier Performance

Filling in the Confusion Matrix

Mastering Confusion Matrix Evaluation