Description

Introduction
Named Entity Recognition (NER) and Text Classification are two powerful techniques in Natural Language Processing (NLP) that help businesses and organizations extract valuable insights from unstructured text data. NER identifies and classifies entities (such as names, dates, locations) from a text, while Text Classification categorizes text into predefined categories (such as spam detection, sentiment analysis, or topic categorization). This course covers both techniques in detail, demonstrating their importance and providing practical applications in real-world business and data-driven environments.

Prerequisites

1. Basic knowledge of Python programming.
2. Familiarity with fundamental machine learning concepts.
3. Understanding of basic text processing methods (tokenization, stopwords removal).
4. Experience with libraries such as pandas, NumPy, or Scikit-learn (optional but helpful).
5. Interest in learning how NLP can be applied to real-world business problems.

Table of Contents

1. Introduction to NLP
   1.1 What is NLP and Why is it Important for Text Analysis?
   1.2 The Role of NER and Text Classification in NLP
   1.3 Common Applications of NER and Text Classification
   1.4 NLP Workflow Overview: Data Collection, Preprocessing, Model Training
2. Named Entity Recognition (NER)
   2.1 Introduction to NER and its Role in Information Extraction
   2.2 Types of Named Entities: Person, Location, Organization, Date, etc.
   2.3 Challenges in NER: Ambiguity, Variability, and Context
   2.4 Rule-based vs. Statistical Approaches to NER
   2.5 Pre-trained NER Models: SpaCy, NLTK, and Hugging Face Transformers
   2.6 Advanced NER: Fine-tuning Models for Specific Domains (e.g., Legal, Medical)
   2.7 Hands-on Exercise: Implementing NER with SpaCy and Custom Entities
3. Text Classification
   3.1 What is Text Classification and Why it is Crucial for NLP?
   3.2 Common Text Classification Use Cases: Sentiment Analysis, Spam Detection, Topic Categorization(Ref: Advanced NLP Techniques: Semantic Analysis and Topic Modeling )
   3.3 Text Preprocessing for Classification: Tokenization, Stemming, Lemmatization
   3.4 Feature Engineering for Text Classification: Bag of Words, TF-IDF, Word Embeddings
   3.5 Supervised Learning Algorithms for Text Classification: Naive Bayes, SVM, Random Forest
   3.6 Evaluation Metrics: Accuracy, Precision, Recall, F1 Score
   3.7 Hands-on Exercise: Text Classification using Scikit-learn with a Sample Dataset
4. NER and Text Classification with Deep Learning
   4.1 Introduction to Deep Learning for NLP: Word Embeddings and RNNs
   4.2 Using Pretrained Transformers for NER and Text Classification (BERT, GPT-3)
   4.3 Fine-tuning Deep Learning Models for Specific Tasks
   4.4 Neural Networks for Named Entity Recognition: Architecture and Training
   4.5 Neural Networks for Text Classification: Convolutional and Recurrent Networks
   4.6 Hands-on Exercise: Implementing NER and Text Classification using Hugging Face Transformers
5. NER Applications in Business
   5.1 Automating Customer Feedback Analysis: Extracting Key Entities from Reviews
   5.2 Using NER for Market Research: Extracting Competitor Information
   5.3 Legal Document Analysis: Extracting Dates, Contracts, and Clauses
   5.4 Case Study: How NER Can Improve Data Accuracy and Compliance in Business
6. Text Classification Applications in Business
   6.1 Sentiment Analysis for Customer Feedback
   6.2 Spam Detection: Filtering Unwanted Content in Emails and Messages
   6.3 Automating Ticket Categorization in Customer Service
   6.4 Use Case: Topic Categorization for News Articles, Blogs, or Social Media
   6.5 Case Study: Improving Customer Experience with Automated Text Classification
7. Advanced Techniques for NER and Text Classification
   7.1 Transfer Learning: Leveraging Pre-trained Models for Faster Deployment
   7.2 Customizing Pre-trained Models for NER and Text Classification Tasks
   7.3 Improving NER Performance with Active Learning
   7.4 Leveraging Multi-class and Multi-label Classification for Complex Business Needs
   7.5 Text Classification in Multiple Languages: Challenges and Solutions
8. Tools and Libraries for NER and Text Classification
   8.1 Overview of Popular Libraries: SpaCy, NLTK, Scikit-learn, Hugging Face Transformers
   8.2 Building a Complete NLP Pipeline with These Tools
   8.3 Fine-tuning and Deploying Models in Cloud Platforms (AWS, Azure, GCP)
   8.4 Real-time Text Classification with Streaming Data
9. Deploying NLP Models in Production
   9.1 Challenges in Deploying NLP Models for Real-world Business Applications
   9.2 Best Practices for Model Deployment: Scalability, Latency, and Monitoring
   9.3 Integrating NLP Models into Business Workflows (CRM, Chatbots, Dashboards)
   9.4 Continuous Improvement: Retraining and Updating Models in Production
   9.5 Case Study: Real-time Text Classification in Customer Service Automation
10. Ethical Considerations in NER and Text Classification
    10.1 Bias in NLP Models: Identifying and Mitigating Unintended Bias
    10.2 Privacy and Data Security: Ensuring Confidentiality in Text Analysis
    10.3 Ethical Use of NER and Text Classification in Sensitive Industries (Healthcare, Finance)
    10.4 Transparency and Explainability in NLP Models
    10.5 Building Responsible AI Systems with NER and Text Classification

Conclusion
Named Entity Recognition and Text Classification are essential NLP techniques for extracting insights from large amounts of unstructured data. Through this course, you will gain hands-on experience in applying NER and text classification models to solve real-world business problems. You will be equipped with the skills to build custom solutions for analyzing customer feedback, automating content categorization, and extracting valuable business intelligence from text data. As NLP continues to evolve, these techniques will remain crucial in developing smarter, more efficient systems that improve decision-making and automation across industries.

Reference