How Important Is An Application Domain In Regards To Post-Graduate In Machine Learning?

July 18, 2019
Artificial Intelligence

 

If you wish to do ML research, either academic or in the industry, then you need to be a great coder and get to working with the elite in the ML domain. But, for the following reasons, you would still have the advantage.

  1. ML research is the right path since there is an acute shortage of qualified practical doctorates in ML. Spending a few years under the best in the domain of ML can actually help improve your knowledge and practical skills through effective mentorship. There are ML mentors like Geoffrey Hinton, Nando Freitas, Yann LeCun, Andrew Zisserman, Andrew Ng, etc who are well known for their work and contribution to research.
  2. Attaining proficiency in Machine Learning Training needs proficiency in data, mathematics, statistics, linear algebra, calculus, differentiation, integration, and a host of other subjects to do research in the ML domain. If you have these it still takes 3-5 years before you get to writing effective algorithms.

Most software engineering jobs in industries do not provide you time for reading or research. Further, you will lose out on practicing your development skills. Since the ML programs on the market today are more or less ready to use, it makes perfect sense to learn Machine Learning Course

To answer which way you should proceed read on. One can opt for any of the two ways of applying ML. To research and applications. Let us explore these choices.

ML research:

Learning about the science of machine learning is actual research. An ML researcher is constantly exploring ways to push the scientific boundaries of the science of ML and its applications to the Artificial Intelligence field. Such aspirants do have a post-graduation or even a Ph.D. in CS with frequent and periodical publications of their research presented at the top ML conferences and seminars. They are visible and popular in these research circles. The ML researcher is looking for something to improve upon and thanks to their efforts technology are always cutting edge and progressing in pace with developments. 

When you need to tweak your applications and seem to go nowhere with it, it is these ML researchers who can get you up from 95 to 98 percent accuracies or more by offering you a personalized and customized solution. The ML researcher really knows his wares well. The only drawback is that he may never get the opportunity to actually deploy his solutions in applications. He knows the theory and is devoid of practice in SaaS delivery, deploying to production or translating the research finding into a practical app.

Machine Learning application:

In comparison to the researcher, the ML application is about the engineering of ML. An ML engineer will take off from where the researcher left. He is adept at using the research and turning it into a valuable practical application or service. They are adept at services of cloud computing and services like the GCP of Google or AWS from Amazon. They are fluent in Agile practices and can diagnose and troubleshoot anywhere in the SDLC of the product.

These ML engineers are often not as recognized as the ML researcher for want of a decorated Ph.D. and referral citations. But they are the people you must go to if you want your customers to be happy with ML-driven products. These application engineers have years of experience and deployments of thousands of products to their credit. 

Consult an ML application engineer before you deploy products or services in the market. Your decisions should be based on your business domain, the product or services on offer and the methods of delivering it to the targeted market. 

Expected payouts:

The Gartner report states that by 2020 the domains of ML and AI will generate 2.3 million jobs. Digital Vidya claims the ML career is great since the inexperienced freshmen land jobs that pay 699,807- 891,326 Rs. If your domain expertise is in data analysis and algorithms your salary could be 9 lakh to Rs 1.8 crore Rs pa.

Conclusion:

For most teams/businesses and teams, ML has many apps that are applicable to its specific needs. You do not need to reinvent it but must know how to use it better. Its an awesome tool for the enterprise and customer’s too! Learn ML at Imarticus Learning. Besides learning how to tweak the ML algorithm through hands-on assignments, project work, and workshops you get assured placements, soft-skill, and personality development modules with a resume writing exercise. Hurry and start today!

 

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