Businesses across sectors are adopting artificial intelligence (AI) and machine learning (ML) at a rapid pace. Massive research work is going on worldwide to explore and exploit the tremendous opportunities that artificial intelligence and machine learning (AI/ML) offer. For current and aspiring professionals, understanding the core concepts and its application thoroughly becomes necessary before diving deep into the AI/ML world. To gain such in-depth understanding, it becomes essential for them to choose an institute that is known for its academic expertise, scientific inquiry, research, apart from industry projects and hackathons. Here’s why?

  1. Artificial intelligence (AI) and machine learning (ML) are yet to reach their full technological potential. A lot of knowledge about, and around them is always lurking in deep research projects and documents that academic teams are privy to. There is a slow pace of enterprise adoption of artificial intelligence and machine learning. Unlike Cloud or Open Source technology, the field of artificial intelligence and machine learning is still in progress on a fundamental level.
  2. A significant portion of exploration, experimentation and research in the domain of artificial intelligence and machine learning is still happening in universities and academic labs. The projects underway in key academic institutions are the harbingers that even the industry looks on to before proceeding on corporate investments. Many premises are still being planned, tested and sharpened at an elementary level at these institutions. The application side is developing, but it is following on the heels of top-tier academic work. The trend may not change in the immediate future.
  3. Artificial intelligence and machine learning are, by their innate nature, radical technologies. They have a considerable possibility of upending a lot of existing models, applications and processes. Looking at them needs a very comprehensive approach that can only be provided consistently by academic experts. We can only cover the full breadth and length of these technologies to a reasonable level by studying them under the wings of an academic expert.
  4. These areas are still struggling with a lot of ethical, pragmatic and governance issues. They need a wide range of collaboration and think-tank inputs to ensure that any malicious use of these technologies is averted or mitigated. Hence, the perspective of an academic expert is far more valuable than that of a working professional in these realms. The degree and depth of ethical debate and boundary-checks that an academic expert can guide with are invaluable, especially in artificial intelligence and machine learning.

Unusual, but true. These AI and ML technologies are still evolving in terms of development, comprehension and application. Therefore, a learner will learn them in a neutral, holistic and pragmatic way when the learning module is guided by, or complemented by, an academic element. Also, the quintessential mathematical roots that these technologies arise from need a solid grip on areas like Statistics, Bayesian logic, Probability, Markov models, data models, linear programming and algorithms. That is something that warrants the need of a mathematics professor or a subject-matter expert of the high cadre.

AI and ML are definitely at early phases of development, but their potential is hugely explosive and massive. Gartner has predicted that by 2020, AI will become a positive net job motivator. It has been reckoned to be creating 2.3 million jobs while only eliminating 1.8 million jobs. As per Deloitte’s Tech Trend 2019 report, as AI adoption grows, companies will increasingly value expertise in areas like data science, algorithm development, and AI-system design focusing on human-centred design experience.

To gain proficiency in these new-age skills and invest in a training course that lets you learn from the best–by partnering with top academic institutions.