AI ML is a hot trending topic — and with whatever is trending comes a flurry of myths and misconceptions. In this article, I have highlighted the six myths that surround the machine learning project and how certification from a top machine learning institute in Bangalore can prepare you to skirt under these misconceptions.

Machine Learning has come a long way since the invention of the first computing device. We have seen the rise of giant computer hardware and software companies like IBM, Microsoft, and others closely linked to their mastery over Artificial Intelligence and Machine Learning. But, now, we are surrounded by a league of startups and DevOps companies that are also expounding their prowess in the industry, largely driven by their vision to bring AI ML to the center of every business operation. If there is one thing that any human can predict about AI ML’s role today, then it has to be the kind of impact it is making or will make on our jobs.

Here are the top myths that are related to any Machine Learning project.

Myth 1: You Need to have Mastery over Maths and Statistics

Top data scientists are average performers when it comes to mathematical and statistical skills. What any ML project truly demands is the ability to understand the problem and how data available to them can be used to make actionable insights. The ML models have become more agile and efficient in the way they work with data, and therefore, top Machine Learning institute in Bangalore enable students with highly advanced problem solving skills and train them on industry centric projects.

Myth 2: You Need a Giant Team to Build ML Projects

This is another myth that plagues ML projects, and we are the product to debunk this misconception with an example. An average ML project in Google or IBM has less than 5 team members and only 2 data analysts that work with historical data. The success of any ML project lies in the ability of the team members to coordinate various operations and make iterative improvements in the machine learning models using advanced data management and analytics.

Myth 3: I will Work on an ML Project all my Life

Most companies have at least 2 product centric machine learning teams that have 5 members each. So, you could be working with 10-15 data analysts, and engineers trained from top Big Data and AI ML courses in India. However, the projects take anywhere between 2-3 years to go from starting point to market readiness. By then, half the team members either move out of the company or are relocated to different projects. Therefore, it is up to 5 members of the core ML building team to take care of the project.

If you are lucky, your hard work will pay off in the form of a successful product that may not need any more improvements or iterations. This means you can focus on building other products in the same company.

Myth 4: Conversely, I may not work on an ML Project for more than 6 Months!

Yes, that’s right — most newly trained machine learning engineers and data analysts move out of projects way too soon before the first iteration comes. On average, a first time ML scientist may not be working on a project for more than 3 months. The reason, the product building team doesn’t want to leak out the key dimensions of the product launch to a newbie. However, if you come into the project with a powerful background in industry training from a top machine learning institute in Bangalore, your tenure could extend to up to 2 years.

One truth that nobody can deny in the ML industry is this — The longer you train with the same team on a multi-verse project, the higher your value will be in the eyes of the hiring managers.”

Myth 5: ML Projects will Pay me all my Life

This is the biggest misconception among the fresh professionals. Unless a company like Google or IBM buys out your ML models or agrees to co-develop with you in an Open source environment, you will continue to be paid in a band that averages out at 14 Lakhs per annum or thereabout.

At our machine learning institutes across India, we train students to gain ownership of their AI ML models and garner royalty / patent that will help them earn more from the industry.

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