Training on Text Analytics with Natural Language Processing (NLP)

This course provides a comprehensive introduction to Text Analytics using Natural Language Processing (NLP). It is designed to help participants understand the techniques and tools used to analyze, process, and derive insights from textual data. The course covers key concepts such as text preprocessing, tokenization, sentiment analysis, topic modeling, and text classification. Through hands-on exercises and real-world examples, participants will learn how to apply NLP techniques to various applications, including sentiment analysis, customer feedback analysis, and automated content generation.

Course Duration

5 Days

Who Should Attend

• Data scientists
• Data analysts
• Machine learning engineers
• Business analysts
• Anyone interested in leveraging text data for insights

Organizational Impact

• Unlocks insights from unstructured text data to understand customer sentiment, market trends, and operational issues.

• Enables actionable intelligence for better products, services, and customer satisfaction.

• Automates tasks like support ticket routing and spam detection, improving efficiency and reducing costs.

• Strengthens competitive position through faster, data-driven responses to market signals.

Personal Impact

• Develops specialized skills in NLP and text analytics, essential for senior data science or analytics roles.

• Empowers participants to transform unstructured data into valuable business insights.

• Builds confidence to lead and champion advanced text analytics initiatives.

By the end of this course, participants will be able 

• Understand the fundamentals of natural language processing
• Preprocess text data for analysis
• Apply various NLP techniques to extract information from text
• Build text classification and sentiment analysis models
• Utilize NLP for information retrieval and summarization

Module 1: Unlocking the World of Natural Language Processing (NLP)

• Discover how machines understand human language
• What NLP is and why it matters
• Tokenization, stemming, lemmatization & stop-word removal
• NLP applications across industries
• Case Study:How global tech companies use NLP for personalization and automation
• Practical Session:Build a simple preprocessing workflow (tokenization & stop-word removal)

Module 2: Transforming Raw Text Into Usable Data – Text Preprocessing

• Prepare and refine text for deeper analysis
• Text cleaning and normalization
• Handling incomplete or noisy text
• Bag-of-Words & TF-IDF
• N-grams and basic language models
• Case Study:Cleaning and preparing social media data for large-scale analysis
• Practical Session:Clean a real text dataset and generate TF-IDF & N-gram features

Module 3: Crafting Powerful Features – Feature Engineering for Text

• Turn text into meaningful numerical insights
• Text feature extraction
• Feature selection & dimensionality reduction
• Word embeddings (Word2Vec, GloVe)
• Case Study:Use of text embeddings in detecting patterns for fraud prevention
• Practical Session:Train Word2Vec and explore word similarity relationships

Module 4: Building Intelligent Models – Text Classification & Sentiment Analysis

Develop models that read and interpret language

• Supervised text classification
• Naive Bayes, SVM, Random Forest
• Sentiment analysis techniques
• Model performance metrics
• Case Study:Sentiment analysis for brand monitoring and customer insights
• Practical Session:Build and evaluate a sentiment classifier using real text data

Module 5: Exploring Cutting-Edge NLP

Step into advanced and emerging NLP technologies

• Named Entity Recognition (NER)
• Text summarization
• Information retrieval
• Natural Language Generation (NLG)
• Case Study:Automated summarization and entity extraction in news intelligence
• Practical Session:Extract named entities and generate short text summaries