Text Analytics & Natural Language Processing Training Course

Description

This course equips participants with practical knowledge and technical skills in text analytics and Natural Language Processing (NLP) for extracting insights from unstructured text data. It covers text preprocessing, language modeling, sentiment analysis, topic modeling, named entity recognition, machine learning for NLP, and real-world applications across business, research, and digital transformation. Participants will learn how to transform textual data into actionable intelligence using modern NLP tools and techniques.

Target Groups

Data analysts and business intelligence professionals
Data scientists and machine learning engineers
Researchers and academics
Software developers and AI practitioners
Digital transformation teams
Customer experience and marketing analysts
Monitoring and evaluation specialists
Public sector data teams
Anyone working with text-based datasets and language-driven insights

Course Objectives

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

Understand core concepts of text analytics and NLP
Prepare and clean textual datasets for analysis
Apply text mining techniques to extract insights
Perform sentiment and opinion analysis
Build topic models for document classification
Use named entity recognition and information extraction techniques
Develop machine learning models for NLP tasks
Analyze text patterns for business and operational decision-making
Apply NLP tools and libraries to real-world datasets
Design text-based analytics workflows and reporting frameworks

Course Modules

Module 1: Introduction to Text Analytics and NLP

Overview of text analytics and NLP
Structured vs. unstructured text data
Common NLP applications and use cases
NLP workflow and ecosystem
Challenges in language data analysis

Module 2: Text Collection and Data Preparation

Collecting text from documents, surveys, and digital platforms
Text cleaning and normalization
Tokenization and stemming
Lemmatization techniques
Handling stop words and special characters

Module 3: Text Representation and Feature Engineering

Bag-of-Words models
Term Frequency–Inverse Document Frequency (TF-IDF)
Word embeddings and vectorization
Sentence embeddings and contextual representations
Feature selection for NLP tasks

Module 4: Sentiment Analysis and Opinion Mining

Sentiment classification techniques
Polarity detection
Emotion and intent analysis
Customer feedback analytics
Social media sentiment monitoring

Module 5: Topic Modeling and Document Classification

Topic modeling concepts
Latent Dirichlet Allocation (LDA)
Text clustering techniques
Automated document classification
Categorizing large text datasets

Module 6: Named Entity Recognition and Information Extraction

Identifying names, organizations, and locations
Entity linking and extraction workflows
Relationship extraction techniques
Text summarization methods
Knowledge extraction from text

Module 7: Machine Learning for NLP

Supervised learning models for NLP
Classification and regression for text
NLP model training and evaluation
Feature engineering for predictive models
Practical model deployment considerations

Module 8: Deep Learning and Advanced NLP

Neural networks for NLP
Recurrent Neural Networks (RNNs) and LSTMs
Transformers and attention mechanisms
Large Language Models (LLMs) and text generation
Fine-tuning pre-trained NLP models

Module 9: NLP Applications and Business Use Cases

Customer support automation
Search and recommendation systems
Survey and feedback analysis
Media and reputation monitoring
Policy, governance, and research applications

Module 10: Capstone Project and Case Studies

End-to-end text analytics project using real-world datasets
Sentiment analysis case study
Topic modeling and classification exercise
NLP dashboard and reporting presentation
Emerging trends in multilingual NLP, generative AI, conversational AI, retrieval-augmented systems, and enterprise language intelligence