Training on Natural Language Processing (NLP) for Text Data Analysis

This advanced course empowers professionals to harness Natural Language Processing (NLP) techniques for extracting insights from unstructured text. Participants will learn practical skills in text preprocessing, classification, sentiment analysis, and building NLP applications such as chatbots and document analysis systems. Through hands-on projects, learners will explore how NLP supports customer feedback evaluation, policy review, and intelligent automation in various sectors.

Duration

10 Days

Who Should Attend

• Data Scientists and Analysts

• Machine Learning Engineers

• Software Developers and AI Practitioners

• Researchers and Academics

• Government Officers handling documents and feedback systems

• Professionals in Customer Service and Market Research

Course Level: Intermediate to Advanced

Organizational Impact

• Extract actionable insights from unstructured text data to improve products, services, and customer satisfaction.

• Automate tasks like support ticket routing and spam detection, boosting efficiency and reducing costs.

Personal Impact

• Gain specialized NLP skills for senior data science, analytics, or business intelligence roles.

• Drive strategic value by transforming text data into actionable business intelligence.

By the end of this course, participants will be able to:

• Understand the foundations and applications of NLP

• Clean, process, and analyze unstructured text data

• Implement sentiment analysis, topic modeling, and classification

• Build NLP tools for tasks like document analysis and chatbot development

• Apply NLP to real-world datasets for strategic insights

Module 1: Introduction to NLP and Text Data

• Overview of NLP and its real-world applications

• Types and sources of text data (e.g., customer reviews, reports)

• Key challenges in analyzing unstructured text

• Tools and libraries for NLP: NLTK, spaCy, Hugging Face

Module 2: Text Preprocessing and Normalization

• Tokenization, stemming, and lemmatization

• Removing stop words, noise, and punctuation

• Text normalization techniques (lowercasing, spelling correction)

• Converting text to numerical format (Bag-of-Words, TF-IDF)

Module 3: Sentiment Analysis and Opinion Mining

• Techniques for analyzing sentiment in feedback and social media

• Rule-based vs. machine learning sentiment classifiers

• Applications in customer experience and service improvement

• Case study: Sentiment analysis for customer feedback

Module 4: Text Classification and Topic Modeling

• Building and training classification models (Naive Bayes, SVM)

• Multi-label classification and evaluation metrics

• Unsupervised methods: LDA and NMF for topic modeling

• Extracting insights from unstructured text for decision support

Module 5: Named Entity Recognition and Text Summarization

• Introduction to entity extraction and recognition (NER)

• Rule-based and deep learning approaches to NER

• Automatic summarization methods (extractive and abstractive)

• Use cases: Policy review, media monitoring, and alerts

Module 6: NLP for Government and Legal Document Analysis

• Structuring and analyzing government reports and regulations

• NLP for transparency, audit, and policy insights

• NLP for government document analysis and automation

• Real-life project: Analyzing legislation using NLP tools

Module 7: Chatbots and Conversational AI

• Designing conversational flows and intents

• Rule-based vs. AI-powered chatbot frameworks

• Tools: Rasa, Dialogflow, and OpenAI APIs

• Case study: Building chatbots for HR and support services

Module 8: Deep Learning for NLP

• Word embeddings (Word2Vec, GloVe, FastText)

• Sequence models (RNNs, LSTMs, GRUs) for text data

• Transformers and pre-trained language models (BERT, GPT)

• Comparing classical vs. deep learning NLP pipelines

Module 9: Evaluating NLP Models and Performance Metrics

• Metrics: Precision, recall, F1-score, confusion matrix

• Cross-validation techniques for NLP

• Bias and fairness in NLP algorithms

• Model tuning and optimization for better generalization

Module 10: Real-World Applications and Final Project

• Analyzing text data with NLP across sectors

• Industry applications: Finance, healthcare, marketing, governance

• Project: Build and present a complete NLP solution

• Roadmap for scaling NLP in your organization