Natural Language Processing (NLP) Explanation of Chapter 6 Text Classification [Video]

Welcome to Chapter 6 of our “Natural Language Processing (NLP) Interview Practice Q&A” series! In this episode, we explore Text Classification, a foundational NLP task that involves categorizing textual data into predefined labels. Text classification powers numerous applications, from spam detection to sentiment analysis, making it a vital skill for NLP professionals and interview candidates alike.

What You’ll Learn:
Introduction to Text Classification:
Understand the significance of text classification and its role in organizing and analyzing textual data.
Explore common use cases, such as email filtering, sentiment analysis, and topic detection.
Key Concepts in Text Classification:
Feature Extraction: Learn how to represent text as features using methods like bag-of-words, TF-IDF, and word embeddings.
Model Selection: Discover popular algorithms, including Naïve Bayes, Support Vector Machines, Decision Trees, and deep learning approaches.
Evaluation Metrics: Understand performance metrics such as accuracy, precision, recall, and F1-score.
Advanced Techniques:
Neural Networks for Text Classification: Dive into neural models like RNNs, CNNs, and transformers.
Transfer Learning: Explore pre-trained models like BERT and GPT for state-of-the-art text classification.
Handling Imbalanced Data: Learn techniques like oversampling, undersampling, and class weighting to address skewed datasets.
Challenges in Text Classification:
Explore issues like noisy data, domain-specific vocabulary, and the need for large labeled datasets.
Understand how to address ambiguity and overlapping categories.
Real-World Applications:
Examine how businesses use text classification for customer feedback analysis, content moderation, and document categorization.
Learn about automated solutions in social media monitoring, legal document tagging, and news categorization.
Interview Practice Q&A:
Prepare for common interview questions about feature engineering, algorithm selection, and performance evaluation.
Gain insights into practical coding challenges, such as implementing a text classifier in Python using scikit-learn or TensorFlow.
Why Text Classification Matters:
Text classification enables the automation of tasks that would be tedious and time-consuming for humans to perform.
Mastering this skill is critical for roles in AI development, data science, and computational linguistics.
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