Natural Language Processing
552 learners
Introduction 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.
Pandas
Python
Scikit-learn
6 lessons
30 practices
6 hours
Machine Learning Modeling for NLP
Lessons and practices
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
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