Applied Text Mining and NLP (Foundations)

Course Overview
This course introduces participants to the essential concepts, techniques, and tools used in analyzing and extracting
insights from unstructured text data. The course provides a balance of theory and practice, covering key NLP
foundations such as text preprocessing, feature extraction, sentiment analysis, topic modeling, and language models.
Through hands-on exercises and real-world case studies, participants will gain the ability to transform raw text into
actionable knowledge and apply text mining techniques to diverse problem domains.

Who Should Attend?
This course is designed for professionals, researchers and data analysts, who are interested in leveraging text data for
decision making, research, or business applications. It is suitable for those with a basic understanding of data analysis
or programming who want to build foundational skills in text mining and NLP, as well as those seeking to transition
into data science or AI related roles.

Course Outcomes
Delegates will gain the skills and knowledge to:
• Understand the core concepts and workflow of text mining and natural language processing (NLP).
• Clean, preprocess, and transform text data for analysis and modelling.
• Apply techniques such as tokenization, stemming, lemmatization, and part-of-speech tagging.
• Build and evaluate models for text classification, sentiment analysis, and topic modelling.
• Use NLP libraries such as NLTK, spaCy, and scikit-learn to perform applied text analysis.
• Interpret model outputs to extract insights and support data-driven decision-making.

Key Course Highlights
At the end of the course, you will understand;
• Fundamental concepts of text mining and natural language processing (NLP).
• Techniques for text preprocessing including tokenization, normalization, and vectorization.
• Use of linguistic concepts and tools like regular expressions, NLTK, and SpaCy for text analysis.
• Methods for text classification and sentiment analysis.
• Approaches to topic modelling and clustering to extract themes from large text datasets.
• Practical applications of NLP in real-world scenarios such as social media mining and customer feedback
analysis.
• Ethical considerations and challenges in text mining and language processing.

Course Booking

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