Qwen AI for NLP Training in Yangon

Qwen AI for NLP Training in Yangon, Myanmar

Participants will gain practical experience in building NLP applications using Qwen models, including text classification, summarization, translation, question answering, and chatbots.

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Qwen AI for NLP Training is a professional training program delivered by ProgNXT in Yangon, Myanmar . ProgNXT, a globally recognized corporate training provider in 55+ countries, offers this Qwen AI for NLP Training course with hands-on, instructor-led sessions. This course introduces participants to Qwen, the open-source LLM family developed by Alibaba, and its application in Natural Language Processing...

Expert Panel

Designed by the ProgNXT AI & Data Science Expert Panel, specializing in Generative AI, Machine Learning, and ChatGPT applications

ProgNXT AI & Data Science Expert Panel

Course Code: EPQ77
14 Hrs
Last Updated: April 2026

Course Rating5/5

This course introduces participants to Qwen, the open-source LLM family developed by Alibaba, and its application in Natural Language Processing (NLP) tasks. The course will explore Qwen’s architecture, fine-tuning capabilities, prompt engineering techniques, and its integration into enterprise NLP pipelines. Participants will gain practical experience in building NLP applications using Qwen models, including text classification, summarization, translation, question answering, and chatbots.

Welcome to the official Qwen AI for NLP Training certification program in Yangon, Myanmar . This comprehensive training is designed to elevate your professional skills and provide you with practical, industry-relevant knowledge locally. As a globally recognized corporate training provider operating in 55+ countries, ProgNXT ensures that our curriculum meets the highest standards of excellence.

Whether you are looking to upskill your team or advance your personal career, our expert-led sessions will guide you through the core concepts of this domain. Upon successful completion of the 14 Hrs program, participants will receive a globally accepted certification, demonstrating their proficiency and readiness to tackle complex challenges in the field.

Understanding of core NLP concepts (tokenization, embeddings, etc.)
Basic proficiency in Python programming
Familiarity with AI/ML workflows and HuggingFace or similar libraries
(Optional) Prior experience with LLMs or Transformers

By the end of this course, participants will be able to:

Understand Qwen model architecture and NLP capabilities
Use pre-trained Qwen models for a variety of NLP tasks
Apply prompt engineering techniques specific to Qwen
Fine-tune Qwen models for custom datasets and domain-specific tasks
Deploy Qwen-powered NLP models in applications

Working with Qwen LLMs for NLP
Prompt crafting and prompt tuning for Qwen
Fine-tuning Qwen using LoRA/PEFT techniques
Performing tasks like summarization, Q&A, classification
Evaluating model performance using NLP metrics

Qwen model architecture and tokenization
Prompt engineering and chain-of-thought prompting
NLP task implementation (classification, QA, summarization)
Fine-tuning techniques and training pipelines
Model evaluation and benchmarking

Participants who attend all sessions and successfully complete the course assessments will receive a ProgNXT Certificate of Completion.

Foundations and Text Processing

Module 1: Introduction to Qwen for NLP

Overview of Qwen’s architecture and NLP capabilities
Setting up the development environment and accessing Qwen API
Exploring key NLP-focused features: text generation, sentiment analysis, entity recognition

Module 2: Advanced Text Processing with Qwen

Text generation and language modeling
Sentiment analysis and emotion detection
Text summarization and paraphrasing
Entity recognition and text classification
Hands-on exercises

Module 3: Integrating Qwen into NLP Workflows

APIs and libraries for seamless integration
Building pipelines for text preprocessing and analysis
Deploying Qwen models in production environments
Demonstration: End-to-end NLP workflow

Customization, Evaluation, and Applications

Module 4: Customization and Fine-Tuning

Adapting Qwen to specific NLP tasks
Training custom models with domain-specific data
Techniques for improving model performance
Hands-on

Module 5: Evaluation and Performance Optimization

Metrics for assessing NLP model quality
Evaluating Qwen’s output and performing error analysis
Optimizing computational efficiency
Demonstration: Improving model outputs and runtime

Module 6: Case Studies and Best Practices

Applications of Qwen in industry-specific NLP tasks
Best practices for large-scale deployment
Addressing challenges and limitations of Qwen
Group discussion: Lessons learned from NLP implementations

Module 7: Summary and Next Steps

Recap of key concepts and workflow design principles
Roadmap for advanced NLP development with Qwen
Q&A and course wrap-up

Lecture slides and code notebooks
Prompt engineering guides and NLP datasets

Lab Assignments
Course Quiz on Qwen + NLP

ProgNXT Certificate of Completion will be awarded to participants who successfully complete the course and assessments.

Qwen AI for NLP Training Events in yangon

Online Events

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More Upcoming Session Dates

November 2026

🌐 Online Instructor-Led

9th Nov 2026→10th Nov 2026

📍 Yangon, Myanmar

12th Nov 2026→13th Nov 2026

🌐 Online Instructor-Led

24th Nov 2026→25th Nov 2026

📍 Yangon, Myanmar

26th Nov 2026→27th Nov 2026

December 2026

📍 Yangon, Myanmar

5th Dec 2026→6th Dec 2026

🌐 Online Instructor-Led

6th Dec 2026→7th Dec 2026

📍 Yangon, Myanmar

20th Dec 2026→21st Dec 2026

🌐 Online Instructor-Led

25th Dec 2026→26th Dec 2026

January 2027

🌐 Online Instructor-Led

1st Jan 2027→2nd Jan 2027

📍 Yangon, Myanmar

12th Jan 2027→13th Jan 2027

🌐 Online Instructor-Led

21st Jan 2027→22nd Jan 2027

📍 Yangon, Myanmar

23rd Jan 2027→24th Jan 2027

Events for other courses in Yangon, Myanmar

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Qwen AI for NLP Training in Yangon