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Is it really worth investing in a machine learning certification course in 2022?

machine learning certification

 

Machine learning has emerged as an important branch of Artificial Intelligence (AI). The recent gush of investments in the machine learning development and research around the world significantly influences the way certification courses are designed and delivered to professionals. Today, large organizations are focusing on AI and machine learning investments that are helping to improve the quality of life and ease of doing business.

It is especially important to note the way and pace at which investors are eyeing newer avenues to invest their funds to build novel frameworks for machine learning algorithms. A majority of the businesses attract AI investors who invest heavily by simply looking at one factor – the talent pool. This talent pool can be created by hiring skilled AI professionals certified with badges earned from the top machine learning certification course in India. With the pandemic effects easing, there are a lot many opportunities available for AI Machine learning engineers and analysts certified in these courses. But, if you really want to make these counted, there are certain things you should keep in mind before starting your journey in this field.

In this article, we have pointed out the recent trends in the Machine learning world that you should know about.

Demand for machine learning talent is at an all time high.

Companies are expanding their AI vision by hiring more talent from the available marketplace. However, only 3 percent of the global companies are able to meet their hiring targets.

Skilled talent in machine learning is considered as a high value asset in any organization. In fast growing industries such as Digital marketing and sales, online advertising, financial services, IT and security, and telecom infrastructure which support traditional business models such as healthcare, manufacturing, retail, and education, the gap between demand and availability of trained ML engineers and data scientists is very large. In some cases, 1 ML engineer is found to be doing the work of 10 engineers, which points to the rather abysmal talent acquisition. The push for AI professionals is putting intense pressure on the talent pools and hiring is not really picking pace despite all the good things we hear about the job titles, considered to be the most appealing and best paying job titles in the world in the 21st century.

Allegedly, AI courses are gaining massive popularity among individual practitioners as well as organizations that look to train their existing workforce in AI and machine learning knowledge. 

AI jobs have failed to evolve.

In the larger context, for the sake of clarity, the AI industry relies on the top AI Ml courses for job redesigning and engineering profiles. 

AI jobs still depend heavily on the way data science is taught. AI trainers and data scientists in particular have failed to establish the importance of AI in how jobs are perceived in the corporate world. For example, AI in the marketing department would work independently of those deployed for Finance, IT, or HR. This soiled approach within the enterprise causes friction among the AI teams working for different teams – and in turn, creates a distorted image of the enterprise which although aspires to be an AI centric company, but fails to achieve its goals. 

The blame has to be squarely placed on job designs for AI engineers. The emergence of newer approaches such as ML Ops, Auto Machine learning, unsupervised machine learning, augmented intelligence, Embedded AI, and so on, seemingly produces different personalities for the same AI machine learning jobs. 

AI engineers no longer need coding skills.

Thanks to the spread of AI education by the courses in India, recruiters too have understood that it is OK to be coming from a non-technical background and still be taking on a role of a data analyst or ML developer.

For ages, the daily task of AI and Machine Learning engineers included working extensively with programming languages and data analytics tools. Thinking of an ML career without a programming background would have been impossible just a few years ago. But today, you are more likely to see ML and AI analysts coming to the forefront with little or no programming experience. This is due to the popularity of open source coding platforms such as Python and R. However, having a programming experience in Java, Python, R, and SQL definitely helps further the career in data science where AI and machine learning projects have a radical scope to transform company’s fortunes. 

AI courses are totally worth it! 

Amy Rey
the authorAmy Rey
Lifelovesu2@gmail.com

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