This course provides a comprehensive exploration of Text Analytics utilizing Natural Language Processing (NLP) tools. Participants will engage in hands-on projects that emphasize practical applications of NLP techniques, enabling them to extract meaningful insights from textual data. The interactive nature of the course encourages collaboration and knowledge sharing, culminating in the opportunity to publish results in Cademix Magazine, showcasing participants’ achievements and innovations in the field.
Throughout the program, learners will delve into various NLP tools and methodologies, gaining a robust understanding of how to implement these technologies in real-world scenarios. The curriculum is designed to equip participants with essential skills that are highly sought after in today’s job market, ensuring they are well-prepared to tackle challenges in data analysis and text processing. By the end of the course, participants will have developed a final project that demonstrates their ability to apply text analytics techniques effectively.
Introduction to Text Analytics and NLP
Overview of Natural Language Processing Techniques
Text Preprocessing: Tokenization, Lemmatization, and Stemming
Sentiment Analysis: Techniques and Tools
Topic Modeling: LDA and Other Approaches
Named Entity Recognition: Methods and Applications
Text Classification: Supervised vs. Unsupervised Learning
Building NLP Pipelines with Python Libraries
Real-time Text Analytics Applications
Final Project: Implementing a Text Analytics Solution