Why this course?

Description

Overview

outlines the structure, project work, and reporting format for students undertaking an internship or capstone in Natural Language Processing (NLP). Covering foundational data manipulation, machine learning models, and advanced applications using large language models, it ensures a practical and comprehensive understanding of NLP pipelines.

Objectives

• After completing this chapter, learners will be able to:
• Preprocess and analyze large-scale textual data.
• Apply machine learning algorithms to natural language datasets.
• Utilize deep learning and transformer models for tasks such as classification, summarization, and generation.
• Document project work through a formal and professional report.

Section 1: NLP Project Highlights

Project 1: Sentiment Classification using Amazon Reviews

Objective: Build and evaluate a sentiment classifier for customer product reviews.

Key Steps:

• Text cleaning and preprocessing
• Feature extraction with TF-IDF or word embeddings
• Training ML/DL classifiers

Project 2: Sales Prediction for FMCG Products

Objective: Predict future sales based on historical and contextual features.

Components:

• Data visualization with Seaborn/Matplotlib
• Regression models using scikit-learn
• Feature engineering and evaluation

Project 3: Fake News Detection using Bi-LSTM

Objective: Classify fake news using bidirectional LSTM networks.

Tools and Techniques:

• Tokenization and padding
• Embedding layers
• Bidirectional LSTM architecture

Project 4: Text Summarization using Transformers

Objective: Automatically generate summaries from large documents.

Frameworks:

• HuggingFace Transformers
• Pretrained models (BART, T5)
• ROUGE metric evaluation

Project 5: LLM Applications

Objective: Develop applications using fine-tuned large language models (LLMs).

Use Cases:

• PDF chatbot using LLAMA2
• Blog and SQL query generation
• Video summarization using Google Gemini

Section 2: Final NLP Internship Report Structure

Title Page

• Project title and subtitle
• Student name and institutional details
• Internship duration and mentor details

Abstract

• One-paragraph summary of the project objectives and output

Introduction

• Overview of NLP and relevance to the problem domain

Dataset and Tools

• Description of data sources
• Preprocessing libraries and model frameworks used

Model Development

• ML/DL model architecture
• Hyperparameter selection and tuning
• Validation approach

Results

• Evaluation metrics (accuracy, F1, recall)
• Visualizations (confusion matrix, loss curves)

Challenges and Insights

• Technical hurdles encountered
• Workarounds and insights gained

Conclusion and Future Work

• Summary of project outcomes
• Recommendations for improvement or deployment

References

• Research papers, toolkits, documentation

Appendices

• Sample code blocks
• Charts and screenshots