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.
Lessons and practices
Lesson 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
Lesson 2: Classifying Text with Logistic Regression in Python
Putting Logistic Regression to Work
Debugging Logistic Regression Model
Mastering Text Classification with Logistics Regression
Lesson 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
Lesson 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
Lesson 5: Deciphering Model Accuracy with the Confusion Matrix in NLP
Evaluating Classifier Performance
Filling in the Confusion Matrix
Mastering Confusion Matrix Evaluation
Interested in this course? Learn and practice with Cosmo!
Practice is how you turn knowledge into actual skills.