| Attribute | Detail |
|---|---|
| Format | Online (e-LMS) |
| Level | Intermediate |
| Duration | 4-6 Weeks |
| Certification | e-Certification + e-Marksheet |
| Fee | ₹2499 / $59 |
| Tools | Python Jupyter Notebook Google Colab Microsoft Excel Relevant Online Databases |
About the Building a RAG-Powered Q&A Bot Course
Building a RAG-Powered Q&A Bot dives deep into Building A Ragpowered Q&A Bot.
Gain comprehensive expertise through our structured curriculum and hands-on approach.
Program Highlights
• Comprehensive coverage of Building a RAG from fundamentals to advanced applications
• Hands-on projects and real-world case studies in Science & Technology
• Expert-curated curriculum aligned with current industry standards
• Access to recorded lectures and e-LMS platform for flexible, self-paced learning
• e-Certification and e-Marksheet upon successful completion
• Dedicated mentor support and interactive doubt-clearing sessions
• Practical experience with tools: Course
• Career-oriented training for academic and professional growth in Science & Technology
Course Curriculum
Module 1: AI Fundamentals, Mathematics, and Building A Ragpowered Q&A Bot Foundations
- Implement Building with Education for practical ai fundamentals, mathematics, and building a ragpowered q&a bot foundations applications and outcomes.
- Design Hands with Course for practical ai fundamentals, mathematics, and building a ragpowered q&a bot foundations applications and outcomes.
- Analyze Building with Education for practical ai fundamentals, mathematics, and building a ragpowered q&a bot foundations applications and outcomes.
Module 2: Data Engineering, Preprocessing, and Feature Pipelines
- Implement Building with Education for practical data engineering, preprocessing, and feature pipelines applications and outcomes.
- Design Hands with Course for practical data engineering, preprocessing, and feature pipelines applications and outcomes.
- Analyze Building with Education for practical data engineering, preprocessing, and feature pipelines applications and outcomes.
Module 3: Model Architecture, Algorithm Design, and Building A Ragpowered Q&A Bot Methods
- Implement Building with Education for practical model architecture, algorithm design, and building a ragpowered q&a bot methods applications and outcomes.
- Design Hands with Course for practical model architecture, algorithm design, and building a ragpowered q&a bot methods applications and outcomes.
- Analyze Building with Education for practical model architecture, algorithm design, and building a ragpowered q&a bot methods applications and outcomes.
Module 4: Training, Hyperparameter Optimization, and Evaluation
- Implement Building with Education for practical training, hyperparameter optimization, and evaluation applications and outcomes. Gain hands-on experience and produce real-world projects.
- Design Hands with Course for practical training, hyperparameter optimization, and evaluation applications and outcomes. Gain hands-on experience and produce real-world projects.
- Analyze Building with Education for practical training, hyperparameter optimization, and evaluation applications and outcomes. Gain hands-on experience and produce real-world projects.
Module 5: Deployment, MLOps, and Production Workflows
- Implement Building with Education for practical deployment, mlops, and production workflows applications and outcomes. Gain hands-on experience and produce real-world projects.
- Design Hands with Course for practical deployment, mlops, and production workflows applications and outcomes. Gain hands-on experience and produce real-world projects.
- Analyze Building with Education for practical deployment, mlops, and production workflows applications and outcomes. Gain hands-on experience and produce real-world projects.
Module 6: Ethics, Bias Mitigation, and Responsible AI Practices
- Implement Building with Education for practical ethics, bias mitigation, and responsible ai practices applications and outcomes.
- Design Hands with Course for practical ethics, bias mitigation, and responsible ai practices applications and outcomes.
- Analyze Building with Education for practical ethics, bias mitigation, and responsible ai practices applications and outcomes.
Module 7: Industry Integration, Business Applications, and Case Studies
- Implement Building with Education for practical industry integration, business applications, and case studies applications and outcomes.
- Design Hands with Course for practical industry integration, business applications, and case studies applications and outcomes.
- Analyze Building with Education for practical industry integration, business applications, and case studies applications and outcomes.
Tools, Techniques, or Platforms Covered
Python Jupyter Notebook Google Colab Microsoft Excel Relevant Online Databases
Real-World Applications
- Apply Building a RAG skills directly to academic research, thesis work, and publications
- Build a professional portfolio showcasing practical Science & Technology competencies
- Solve industry-relevant problems using Building a RAG methodologies and tools
- Contribute to open-source projects and collaborative research in Science & Technology
- Prepare for competitive examinations, interviews, and professional certifications in Science & Technology
Who Should Attend & Prerequisites
- Designed for Professionals.
- Designed for Students.
- Foundational knowledge of artificial intelligence and familiarity with core concepts recommended.
- Mentorship by industry experts and NSTC faculty.
Certification
