Talk to our Students Apply Now Convocation 2026

Certification Program in Natural Language Processing (NLP)

Event Date: 23/03/2026 – 07/04/2026

Event brief description:

This 20-hour certification program in Natural Language Processing (NLP) is designed and delivered by the Centre of Supercomputing & Advanced Al Research at Galgotias University, under the School of Artificial Intelligence (SoAI). The program equips participants with both the theoretical foundations and applied skills necessary to work confidently with real-world text data using modern NLP techniques. The curriculum is grounded in the GenAI Training 2026 initiative and leverages the university's NVIDIA DGX H200 supercomputing infrastructure to offer an industry-grade learning environment. The program progresses systematically from core NLP concepts through preprocessing, vectorization, word embeddings, and culminates in a 5-hour capstone project where participants build and evaluate a complete text classification system.

Event Detailed Description

The Certification Program in Natural Language Processing (NLP), organized by the School of Artificial Intelligence (SoAI) in collaboration with the NVIDIA Centre of Supercomputing & Advanced AI Research at Galgotias University, is a 20-hour intensive training designed to equip participants with both foundational knowledge and hands-on expertise in modern NLP techniques. 

This program is structured to bridge the gap between theoretical understanding and real-world application of NLP. It covers the complete lifecycle of text data processing, starting from basic concepts such as tokenization, corpus handling, and preprocessing, to advanced techniques including vectorization, word embeddings (Word2Vec, GloVe, FastText), and machine learning-based text classification. Participants gain practical exposure through tools like Python, NLTK, SpaCy, Gensim, and Scikit-learn, ensuring industry-relevant skill development. 

A key highlight of the program is its strong emphasis on experiential learning. Out of the total duration, a significant portion is dedicated to hands-on lab sessions and a capstone project. Learners work on real-world datasets such as IMDB reviews, SMS spam detection, or news classification to design and implement end-to-end NLP pipelines. This includes preprocessing, feature extraction (TF-IDF and embeddings), model training using algorithms like Naïve Bayes and SVM, and performance evaluation using metrics such as accuracy, precision, recall, F1-score, and confusion matrices. 

The program is delivered through a combination of theory sessions, practical labs, and guided project workshops, ensuring a comprehensive learning experience. Additionally, participants benefit from exposure to advanced infrastructure, including NVIDIA DGX H200 systems, enabling them to understand large-scale AI applications. 

By the end of the program, participants are expected to develop the ability to build, evaluate, and deploy NLP-based solutions, interpret model outputs, and apply learned concepts to real-world problems such as sentiment analysis, text classification, and information extraction. The program concludes with assessments, including lab performance, a capstone project, and a concept quiz, leading to certification. 

Overall, this certification program serves as a comprehensive platform for students and professionals to build strong competencies in NLP, preparing them for academic research, industry applications, and future advancements in Generative AI.

Department Name: School of Artificial Intelligence 

Event Outcome

The Certification Program in Natural Language Processing (NLP) successfully achieved its objective of equipping participants with both theoretical knowledge and practical skills in modern NLP techniques. The event resulted in a significant enhancement of participants’ understanding of core NLP concepts, including text preprocessing, tokenization, vectorization, and machine learning-based text classification.

Participants gained hands-on experience using industry-relevant tools such as Python, NLTK, SpaCy, Gensim, and Scikit-learn. Through structured lab sessions and guided practice, they were able to implement real-world NLP tasks effectively. The exposure to advanced infrastructure, including high-performance computing systems, further enriched their learning experience and provided insights into large-scale AI applications.

A major outcome of the program was the successful completion of capstone projects, where participants developed end-to-end NLP solutions such as sentiment analysis, spam detection, and news classification systems. These projects enabled learners to apply the complete pipeline—from data preprocessing to model evaluation—thereby strengthening their problem-solving and analytical abilities.

The program also fostered collaborative learning, critical thinking, and innovation among students and faculty. Participants demonstrated improved confidence in handling real-world datasets and interpreting model performance using evaluation metrics.

Overall, the event contributed to building a skilled talent pool in NLP and Generative AI, supporting academic excellence, research capabilities, and industry readiness. It also strengthened the university’s ecosystem for advanced AI learning and practical implementation.

  1. Successfully developed end-to-end NLP projects (e.g., sentiment analysis, spam detection, news classification)
  2. Gained hands-on experience in building complete pipelines: preprocessing → feature extraction → modeling → evaluation
  3. Applied machine learning algorithms such as Naïve Bayes and SVM on real-world datasets
  4. Improved coding proficiency in Python and NLP libraries (NLTK, SpaCy, Gensim, Scikit-learn)
  5. Learned to evaluate models using metrics like accuracy, precision, recall, F1-score, and confusion matrix
  6. Successfully contributed to academic research with submission of 5 research papers
  7. Established a foundation for future work in Generative AI and Large Language Models (LLMs)

Related Goal