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Text Classification Using PyTorch

PROJECT

Text Classification Using PyTorch

In this project, we will learn how to build a deep-learning-based classifier using PyTorch. We will learn about text preprocessing, feature extraction, model selection, training, and evaluation. We will use classical Python NLP libraries such as NLTK and explore traditional machine learning algorithms such as XGBoost in addition to the neural networks.

You will learn to:

Clean and extract features from text.

Build and train machine learning and deep learning models.

Use contextualized embeddings and pretrained language models.

Handle imbalanced data effectively.

Skills

Natural Language Processing

Neural Networks

Machine Learning Fundamentals

Deep Learning

Transformer Models

Prerequisites

Intermediate knowledge of Python programming language

Basic knowledge of pandas library

Basic knowledge of machine learning paradigms and techniques

Basic knowledge of PyTorch framework

Technologies

NLTK

Pandas

XGBoost

PyTorch

Scikit-learn

Project Description

Text classification is a core task in natural language processing (NLP) that involves automatically assigning predefined categories to text documents. It powers applications like sentiment analysis, spam detection, topic classification, and AI-generated content detection.

In this project, you’ll learn how to preprocess text data, extract features, and build multiple machine learning models to classify questions into categories. You’ll start with traditional approaches like Logistic Regression and XGBoost, then move to deep learning models that use TF-IDF features, learned embeddings, pretrained GloVe vectors, and transformer-based architectures like MobileBERT.

By the end, you’ll compare all models side by side to evaluate their accuracy and understand the strengths of each approach, from classical ML to modern neural networks.

Project Tasks

Introduction

Task 0: Get Started

Task 1: Import Libraries and Explore Datasets

Data Preparation and Basic Feature Engineering

Task 2: Preprocess Text

Task 3: Split the Data

Task 4: Extract Features (BoW)

Task 5: Extract Features (TF-IDF)

Linear and Tree Models

Task 6: Train a Linear Model

Task 7: Tune Hyperparameters

Task 8: Train an Ensemble Model

Task 9: Evaluate the Model

Neural Networks

Task 10: Define a Neural Network

Task 11: Create Datasets and DataLoaders

Task 12: Set Up Training

Task 13: Train and Evaluate the Neural Network

Task 14: Get Word Embeddings

Task 15: Set Up Training

Task 16: Train and Evaluate the Neural Network

Task 17: Get Embeddings from Pretrained Language Models

Task 18: Set Up Training

Task 19: Train and Evaluate the Neural Network

Data Imbalance

Task 20: Handle Imbalanced Data

Task 21: Train and Evaluate the Neural Network

Task 22: Compare Model Performance

Task 23: Save the Neural Network

Congratulations!

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