Text Classification with Natural Language ProcessingIntroduction to Modeling Techniques for Text Classification

This course paves the way for your journey in NLP modeling. Delve deep into the world of text classification algorithms starting with Naive Bayes, Support Vector Machines, Decision Trees, and Random Forests. But that's not all! You'll also get familiar with stratified cross-validation, a key tool for handling imbalanced classes in text data.

Implement Stratified Cross-Validation in Train-Test Split

Analyzing Spam and Ham Distribution in Train-Test Split

Exploring the Spam Dataset

Stratified Train-Test Split for Text Data

Stratified Train-Test Split and Class Distribution Analysis

Tuning Alpha Parameter in Naive Bayes Model

Fill in the Blanks: Building Naive Bayes Model

Fill in the Blanks: Predicting Using Naive Bayes Model

Visualize Naive Bayes Model Predictions

Evaluate Naive Bayes Model with Confusion Matrix

Switching SVM Kernel to Polynomial

Building and Training a Linear SVM Classifier

Predicting and Evaluating with SVM Model

Training and Predicting with SVM Model

Complete SVM Text Classification Model from Scratch

Adjust Max Depth of Decision Tree Classifier

Implementing Decision Tree Classifier

Generate the Classification Report

Implementing and Visualizing Decision Tree Classifier

Building and Evaluating a Decision Tree Model

Adjusting Parameters of RandomForest Classifier

Fill the Blanks in the RandomForestClassifier Script

Insert Code to Evaluate RandomForest Classifier

Creating and Training RandomForest Classifier

Train and Evaluate RandomForest Classifier

Adjusting Regularization in Logistic Regression Model

Initialize and Train Logistic Regression Model

Prediction and Evaluation of Logistic Regression Model

Improving Logistic Regression Model with Regularization

Implementing Logistic Regression on Text Data