AI in Natural Language Processing
Build and apply AI models to analyze, classify, and understand natural language, gaining practical skills in modern NLP techniques and tools.
PBL
Project
Hugging Face Project
Location
Online
Duration
8 Weeks
Upcoming Sessions
Fall 2025
Outcomes
Apply Python and PyTorch for NLP workflows
Understand transformer architectures in NLP
Evaluate model performance on real-world datasets
Use Hugging Face libraries for practical model training
Fine-tune pre-trained language models for NLP tasks
You Will Get
Industry Guidance
Work directly with our project leads—experts and top researchers—who bring their real-world insights and expertise straight to your learning experience.
Research Experience
Collaborate with teammates and the project lead in a multi-week project to pursue novel questions in your research field.
Peer Networks
Engage with our PBL participants from all over the world. Collaborate with new peers and learn about their own research endeavours.
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A Strong Portfolio
Put your best foot forward in the PBL with a standout project and receive a PBL Evaluation Report that can be used as a recommendation letter for employers and grad schools.
Expert Guidance
Get personalized feedback to grow your research and innovation skills.
Deliverables
Real projects, lasting connections, and new opportunities beyond your program.
Project Deliverables
The final presentation of your 8 weeks could be a poster, written report, or a slide deck, all of which can be expanded on.
Research Extension
Utilize up to 5 additional meeting times with the project lead after the project’s conclusion to build your work out for publication or conference presentation.
Industry Network
Meet peers in your projects and participate in a global talent community both online and in-person.
Industry Application
Hugging Face is a leader in democratizing natural language processing through open-source tools and pre-trained language models widely used in industry and research. This PBL aligns with Hugging Face’s mission by equipping students with practical skills to fine-tune, apply, and deploy AI models for real-world NLP tasks such as text classification, named entity recognition, and question answering, using the same tools leveraged by companies for chatbots, customer insights, and automated content analysis.
Popular Industry Positions
Data Scientist
Analyze and extract insights from unstructured text data.
Machine Learning Engineer
Fine-tune AI models for real-world applications.
NLP Engineer
Build and deploy language models for text understanding.
Tracks
Track 1
Text Classification (Sentiment Analysis)
Students will build sentiment analysis models using encoder-based language models to classify and evaluate text data effectively.
Learn the fundamentals of text classification in NLP.
Fine-tune pre-trained encoder-based models using Hugging Face.
Work with real-world datasets such as product reviews and social media data.
Gain hands-on experience with Python, PyTorch, and transformers.
Understand how to evaluate and improve model accuracy and generalization.
Track 2
Named Entity Recognition (NER)
Students will develop NER models that identify and classify entities within text using encoder-based models and modern NLP workflows.
Understand sequence labeling and NER dataset structures.
Fine-tune encoder models to recognize entities (people, locations, organizations).
Use Hugging Face tools for token-level classification tasks.
Learn practical applications in healthcare, legal tech, and finance.
Build skills for information extraction from unstructured data.
Track 3
Semantic Textual Similarity (STS)
Students will create models to measure the semantic similarity between sentences, enabling advanced text understanding applications.
Explore sentence embeddings and semantic similarity scoring.
Fine-tune models using sentence-transformers and STS datasets.
Learn how to evaluate sentence-level meaning and similarity.
Gain experience with Python, PyTorch, and the Hugging Face ecosystem.
Apply models to tasks like FAQ matching, deduplication, and retrieval systems.
Track 4
Question Answering (QA)
Students will develop QA systems that extract relevant information from texts using encoder-based models for practical NLP tasks.
Learn the fundamentals of extractive question answering.
Fine-tune models on QA datasets to identify answer spans in text.
Apply tools like Hugging Face transformers and PyTorch.
Understand the structure of QA datasets and annotation formats.
Explore applications in virtual assistants, customer support, and knowledge retrieval.
PBL Journey
Online PBL Projects meet once a week for 8 weeks, and follow the research project format. Participants will meet the project lead, learn the conventions of the field and familiarize themselves with the tracks, then spend the middle portion of their time collaborating to develop their research.
At the end, participants will present their final project and receive feedback, with the opportunity to extend their timeline and develop the project in greater depth.
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Project Team
Our Academic Team plays a vital role in your PBL journey at Blended Learning. We are dedicated to enhancing your learning experience and ensuring your academic success. Our team consists of three distinct roles, each with a specific focus to support your Research Guidance, Project Progress, and Personal Growth.
Project Lead
Providing Industry and Research Guidance
Researcher in Machine Learning and NLP, Concordia University
He specializes in analyzing implicit discourse using fine-tuning and prompting of language models. He has published four peer-reviewed papers, with two under review, and developed NLP models for the Government of Québec at the Applied AI Institute. He holds a Master’s in Engineering Physics with a focus on quantum physics and machine learning, leading to a journal publication on quantum phase estimation. He has also worked as a tech consultant on data extraction and transformation projects and has experience teaching undergraduate AI courses.
Academic Advisor
Tracking Your Project Development
The Academic Advisor is dedicated to your project completion success. They manage the progress of your PBL, guiding team formation, facilitating group discussions, and resolving conflicts. Additionally, the Academic Advisor ensures team member contributions are on track and provides logistical support, including attendance tracking, hosting recitation sessions, managing research support requests, and conducting student evaluations at the end of the PBL.
From Our Students
"After a night spent debugging, I suddenly discovered the program running perfectly. In that triumphant moment, you realize your true capability and success. The exhaustion fades, replaced by the thrill of knowing your skills and persistence led to this achievement, reaffirming your potential."
Nicole Y.
National University of Singapore
B.S. Economics
FAQs
What is the learning format of a PBL?
All PBLs are offered in an 8-week online format that begins with an orientation followed by subject setup overview of the different tracks. The majority of the session time is dedicated to project development, with a final presentation at the culmination of the 8 weeks. Many PBLs are also offered bi-annually in an on-campus format that consists of daily in-person meetings.
How long does each PBL cohort last？
One round of the Online PBL cohort lasts 8 weeks, preceded bys a pre-PBL orientation week. Each On-Campus PBL usually has 8 in person meetings, with intensive classroom education and collaboration. This means the biggest difference between online and on-campus PBLs is time participants have in between meetings.
How can I be more academically prepared before the PBL starts?
Review the Blended Learning Insights sent by the Academic Advisor and familiarize yourself with the project topic and pre-learning materials. Ensure you have all necessary softwares and other resources needed for the PBL.
For each PBL cohort, will I work in teams? Are PBL team members self-selected or assigned?
Yes, you will work in teams for each round of the PBL Cohort. Each team has 3 to 6 participants, organized by the Academic Team. The Academic Advisor will organize groupings based on students' backgrounds, preferred track, and skills.
Can I work with the Project Lead on my project after the PBL ends?
Yes, with your AI + X Research Plan, you may request up to five PBL Research Extension meetings, where you work with the project lead to develop your project into a working manuscript. To schedule a PBL Research Extension meeting, talk to your Academic Advisor at the conclusion of your PBL.
What do I receive at the end of the PBL?
At the conclusion of the PBL cohort, you can request a PBL Evaluation Report which summarizes the PBL content, the hours you spent, the track you chose, and includes a recommendation letter from the Project Lead (for eligible participants who completed the project successfully).
Is attendance mandatory for PBL Live Sessions and Recitation Sessions?
Yes, attendance is mandatory for both PBL Live Sessions and Recitation Sessions. Participants with three or more unexcused absences forfeit their eligibility for a PBL Evaluation Report.
Do I need to have my camera on during online PBL Live Sessions?
Yes, you must have your camera on during online PBL Live Sessions. Participants with cameras off will be marked as absent. This is meant to encourage active engagement and participation in meetings.
