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Generative AI for Engineering Students/ Working Professionals banner

Generative AI for Engineering Students/ Working Professionals

Generative AI for Engineering Students/ Working Professionals banner
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Generative AI for Engineering Students/ Working Professionals

4(28)
1345 views
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10 hrs
-
English
1345 views
Kapil Singh
Kapil Singh
AerospaceAutomotive
  • 7-day money-back guarantee
  • Session recordings included
  • Certificate of completion
Volume pricing for groups of 5+

Why enroll

Mastering Generative AI can revolutionize your engineering career, opening doors to cutting-edge roles in AI-driven design, development, and innovation. With this expertise, you'll be in high demand as an AI Engineer, AI Researcher, or AI Consultant, and be competitive for senior roles like AI Technical Lead, AI Innovation Manager, or Chief AI Officer. You'll be empowered to create intelligent systems, automate complex tasks, and drive digital transformation in various industries. Pursue certifications like Certified AI Engineer or Certified Data Scientist to further accelerate your career. Stay ahead of the curve and unlock new opportunities in the rapidly evolving field of Generative AI.

Your instructor

Kapil Singh

Kapil Singh

Chief Evangelist

20+ yrs experience·India

16Courses
52Learners
4Avg rating
1Followers

Is this course for you?

You should take this if

  • You work in Aerospace or Automotive
  • You're a Mechanical Engineering professional
  • You prefer live, instructor-led training with Q&A

You should skip if

  • You need a different specialisation outside Mechanical Engineering
  • You need fully self-paced, on-demand content

Course details

Generative AI is transforming how engineers learn, design, analyze, and solve problems. It enables users to generate text, code, designs, simulations, and insights quickly, making it a powerful tool for both students and professionals.
Generative AI refers to advanced AI models that can create new content such as reports, designs, code, and simulations based on prompts. Popular tools include ChatGPT, GitHub Copilot, and Google Gemini.

This course provides an in-depth exploration of Generative AI, focusing on its principles, methodologies, and applications. Students will gain hands-on experience with state-of-the-art generative models and understand their impact on various engineering domains.

Course suitable for

Key topics covered

Week 1: Introduction to Generative AI

Lecture 1: Overview of AI and Machine Learning

  - Definition and history of AI

  - Fundamentals of AI and ML.

Lecture 2: Introduction to Generative AI

  - Definition and significance

  - Key applications in engineering

Week 2: Fundamentals of Generative Models

Lecture 3: Probabilistic Models

  - Bayesian networks

  - Markov models

Lecture 4: Neural Networks and Deep Learning

  - Basics of neural networks

  - Introduction to deep learning

Week 3: Advanced Generative Models

Lecture 5: Transformer Models

  - Architecture and working principles

  - Applications in text generation

Lecture 6: Diffusion Models

  - Basics and applications

  - Comparison with other generative models

Week 4: Practical Applications in Engineering

Lecture 7: Generative AI in Design and Manufacturing

  - CAD design automation

  - Generative design in manufacturing

Lecture 8: Generative AI in Robotics and Automation

  - Path planning and control

  - Simulation and training of robots

Week 5: Ethical and Societal Implications

Lecture 9: Ethical Considerations

  - Bias and fairness in generative models

  - Privacy concerns

Lecture 10: Societal Impact

  - Job displacement

  - Future trends and opportunities

Week 6: Hands-on Projects and Case Studies

Lecture 11: Project Introduction and Guidelines

  - Overview of project requirements

  - Team formation and project planning

Lecture 12: Case Studies

  - Real-world applications of generative AI

  - Success stories and lessons learned

Course Wrap-up and Future Directions

  - Recap of key concepts and learnings

  - Q&A session

Future Directions in Generative AI

  - Emerging trends and technologies

  - Career opportunities in generative AI

 

Opportunities that await you!

Career opportunities

Training details

This is a live course that has a scheduled start date.

COMPLETED

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Questions and Answers

A: That’s the most common mistake — confusing speed with authority. ISO 26262 is protecting against systematic faults, not clerical effort, so any automated hazard list has to stay advisory while you own the severity, exposure, and controllability logic. The standard cares about accountable engineering judgment and traceability, neither of which transfers to a language model.

A: That’s the most common mistake — equating material strength tables with durability. Fatigue, surface condition, and environmental knockdowns dominate here, and AI tools don’t see your pothole spectra or salt exposure unless you feed them. Without re-deriving those drivers, the comparison is hollow and pushes risk downstream into warranty.

A: That’s the most common mistake — treating catalog life as a black box multiplier. A rough contact stress and load cycle count tied to wheel RPM tells you immediately whether you’re off by decades. If the order doesn’t line up, the detailed math never will.

A: That’s the most common mistake — jumping straight into software. If the transmitter is plumbed wrong or even on the wrong line, every downstream check is wasted effort. Field reality has to align with the drawing before bits and bytes matter.