Introduction

This course provides a comprehensive overview of real-world applications of Natural Language Processing (NLP), equipping professionals with the skills to implement NLP solutions in practical scenarios.

Objectives

• Understand the fundamentals of NLP and its core techniques
• Explore current NLP applications in different industries
• Develop skills to preprocess and analyze textual data
• Build and evaluate NLP models for various tasks
• Apply NLP tools to solve real-world business problems

Course Outlines

Day 1: Foundations of Natural Language Processing

• Introduction to NLP and its significance
• Basic text processing and normalization
• Tokenization and sentence segmentation
• Part-of-speech tagging concepts
• Overview of NLP tools and libraries

Day 2: Text Representation and Feature Engineering

• Bag-of-words and TF-IDF techniques
• Word embeddings and vectorization
• Using pre-trained language models
• Dimensionality reduction for text features
• Preparing data for NLP machine learning

Day 3: Core NLP Tasks and Algorithms

• Text classification and sentiment analysis
• Named Entity Recognition (NER)
• Topic modeling approaches
• Text summarization techniques
• Machine translation basics

Day 4: NLP Applications in Industry

• Chatbots and conversational agents
• Information extraction from documents
• Search and recommendation systems
• Voice assistants and speech recognition
• Ethical considerations in NLP

Day 5: Building and Evaluating NLP Solutions

• Designing NLP project workflows
• Model evaluation and error analysis
• Deployment strategies for NLP models
• Case studies of successful NLP applications
• Future trends in NLP and course wrap-up