National Institute of Robotics & Artificial Intelligence

Frequently Asked Questions

Flexibility: Online courses allow you to learn at your own pace and fit your studies around your busy work schedule. You can access course materials anytime, anywhere.
No Relocation Required: Unlike traditional classroom-based courses, online learning eliminates the need to relocate or commute to a physical campus. This convenience is especially beneficial for working adults.
Career Advancement: Online programs offer credentials such as certificates, professional certifications, or even full degrees. These can help you make significant career changes or advance into managerial roles.
Self-Paced Learning: With online courses, you control the pace. Whether you’re a fast learner or need more time, online education adapts to your needs.
Networking Opportunities: Virtual classrooms, discussion forums, and collaborative projects allow you to connect with professionals worldwide. Networking is crucial for career growth.
Cost-Effectiveness: Online courses often cost less than traditional programs. You save on commuting, accommodation, and other expenses.
Enhanced Work-Life Balance: Balancing work, family, and education becomes easier with online learning. You can study when it suits you.

Flexibility: Online courses allow you to learn at your own pace and fit your studies around your busy work schedule. You can access course materials anytime, anywhere.
No Relocation Required: Unlike traditional classroom-based courses, online learning eliminates the need to relocate or commute to a physical campus. This convenience is especially beneficial for working adults.
Career Advancement: Online programs offer credentials such as certificates, professional certifications, or even full degrees. These can help you make significant career changes or advance into managerial roles.
Self-Paced Learning: With online courses, you control the pace. Whether you’re a fast learner or need more time, online education adapts to your needs.
Networking Opportunities: Virtual classrooms, discussion forums, and collaborative projects allow you to connect with professionals worldwide. Networking is crucial for career growth.
Cost-Effectiveness: Online courses often cost less than traditional programs. You save on commuting, accommodation, and other expenses.
Enhanced Work-Life Balance: Balancing work, family, and education becomes easier with online learning. You can study when it suits you

High Demand: AI and ML professionals are in high demand across industries. Companies seek experts to develop intelligent systems, leading to attractive job opportunities.
Salary: The average base salary for a machine learning engineer in India is around ₹700,000 per annum, making it one of the highest-paying roles.
Growth Potential: The field is expanding rapidly, with projected growth in the coming years. As AI applications multiply, professionals can contribute significantly.
Diverse Roles: AI/ML careers offer roles like Machine Learning Engineer, Data Scientist, NLP Scientist, and Business Intelligence Developer. Each role has unique challenges and rewards.
Continuous Learning: AI evolves constantly, providing intellectually stimulating work. If you enjoy cutting-edge technology, this field offers exciting opportunities.

To start with, these roles require a solid background in computer science, mathematics, and statistics, as well as proficiency in programming languages such as Python and R. Hands-on experience in data handling, preprocessing, and familiarity with machine learning frameworks are essential.
Formal education, like university programs or industry certifications, can offer the necessary knowledge and qualifications. Moreover, gaining practical exposure through projects, competitions, and contributing to open-source initiatives is crucial.
Staying updated with the latest advancements, continuous learning, and staying informed about the evolving AI landscape are key to thriving in this field. With dedication, ongoing skill enhancement, and suitable opportunities, adept AI and ML professionals can carve out a successful and fulfilling career path.

The skills that you may learn in an AI course include:
Data preparation and feature engineering
Model evaluation and validation
Natural Language Processing (NLP)Deep learning
Computer vision
AI ethics and responsible AI
Practical application and project work
The skills you can learn in an ML course include:
Machine learning fundamentals
Supervised learning
Unsupervised learning
Regression analysis
Classification
Model evaluation and validation
Practical application and project work.
Model tuning and optimization
By gaining these skills, professionals can significantly improve their career prospects and enhance their ability to create impactful AI and ML models.

A Bachelor’s degree with mathematics, computer science, or a related field will provide a strong foundation for a career in AI and ML. A basic understanding of mathematics, physics and a popular coding languages can build your skills further.
As AI and ML continue to change and develop, so will the knowledge around it. To keep up with the transformations in the industry, NIRA offers an array of courses that will suit your career requirements and upgrade your knowledge with the latest advancements in the industry. NIRA online courses are created to ensure continuity in your knowledge so that you do not fall behind in a rapidly changing industry.

Individuals aspiring to work in AI and ML have numerous job opportunities available to them that revolve around creating and implementing artificial intelligence and machine learning algorithms. These positions encompass roles like data scientist, AI developer, machine learning engineer, AI researcher, and business intelligence developer. Moreover, data from job search platforms such as Indeed and Turing indicate a high demand for machine learning engineers and AI developers across diverse sectors such as finance, healthcare, education, and beyond. Additionally, professionals can explore roles in specialized areas like Natural Language Processing (NLP), computer vision, and robotics.