Natural Language Processing
485 learners
Building 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.
Pandas
Python
Scikit-learn
5 lessons
20 practices
3 hours
Machine Learning Modeling for NLP
Lessons and practices
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
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