5 reasons machine learning is worth learning & good career in 2022

In this article let’s take a look at what machine learning is all about and whether it is a good career and worth learning or not in detail.

So if you are confused and don’t know whether you should learn machine learning and build a career or not then you should read this article till the end.

So without further ado let’s get started.

5 reasons why machine learning is worth learning and good career?

Growing demand

One of the main reasons why machine learning is worth learning and Good career is because it has a growing demand.

According to a report of world economic forum 6 out of 10 jobs that humans  currently do can get replaced by machines and artificial intelligence by 2025.

And according to McKinsey report, they estimate that anywhere between 400 million and 800 million individuals can lose their job due to automation.

We are actually witnessing many of this reports coming out as true with artificial intelligence slowly replacing jobs in sectors like accounting, proof reading, data entry, book keeping, receptionist, car driving, defence & armed forces, police services, security guards.

One of the real reasons of growing demand of machines and artificial intelligence is it can increase the efficiency and reduce the costs, errors involved compared to doing the work manually.

With growing expansion of Artificial Intelligence and automation, machine learning experts job will also increase because they are the one who will teach the Machines to work without human intervention.

The global machine learning market is actually expected to grow from $8.43 billion in 2019 to $117.19 billion by 2027.

And according to bls, Employment of computer science is expected to grow 22 percent from 2020 to 2030.

And you can find more than 15000 new job openings to apply for machine learning engineers on naukri.com website & 8000 open jobs on indeed website.

Work in different industries

Another important reason is because you can get a chance to work in different industries like, cybersecurity, health care, transportation, armed forces & defence, manufacturing industries, finance, retail & ecommerce, marketing, etc.

And you need to create different artificial intelligence tools by training them with different data in different sectors and industries because millions of jobs are going to get replaced in different industries not just one industry.

Bright future

Another important reason is because it has a bright future.

As you can learn from the above statistics and data that artificial intelligence will replace millions of jobs in the upcoming years but ML engineers will still be in demand because

the very main job of machine learning engineer is all about training machines and feeding them data to automate any task without any human intervention and

as per McKinsey report by 2055 half of the jobs that we do manually can get replaced by artificial intelligence which means that more machine learning experts will be required to train the Machines to do the job in coming future.

According to Indeed, ML Engineer jobs grew 344% from 2015 to 2018 and have an average base salary of $146085.

And machine learning requires data science coding and creativity skills which cannot get replaced by artificial intelligence in the future.

In fact there are many machine learning companies which are always looking to hire very skilled ML engineers because they know the value of them and how they can improve the company like Netflix YouTube Amazon etc, for example Netflix has announced 1 million dollars worth price for any individual who could improve their recommendation feature by at least 10% and this not only helps the company grow its user base but also hire best talent possible in the world.

High paying job

Another important reason why machine learning is a good career and worth learning is because you can get a high paying job from it.

According to Glassdoor the average salary of a machine learning engineer in India is around 8 lakh rupees per annum.

According to SimplyHired.com, the average machine learning engineer salary is $142,000.An experienced machine learning engineer can earn up to $195, 752.

Wide career

Another important reason is you can get wide variety of options for example

you can become machine learning engineer, data analyst, software developer, data scientist, Computational Linguist, etc

Machine learning helps you in two ways, one is for a machine learning engineer job and the other is for a data scientist job. And believe it or not both of them are very popular jobs at the moment.

It means that you can analyse tons of data, extract value and glean insight from it, and later make use of that information to train a machine learning model to predict results.

 In many organizations, a machine learning engineer often collaborates with a data scientist for better synchronization of work products.

Machine learning can analyze and organize patterns and data About your customers’ demographic profiles, choices and preferences, Behaviors, and others.

Machine learning can get these data from online tools and mechanisms that you use, such as emails collected from sign-ups.

Such ability by machine learning enables you to know and understand your customers more quickly. This makes your products more personalized for users depending upon their interests and convenience.

Machine learning is a great career path if you’re interested in data, automation, and algorithms as your day will be filled with analyzing large amounts of data and implementing and automating it.

Conclusion

These are all the important reasons why I believe machine learning is a good career option and definitely worth learning.

Do comment down what do you think about machine learning in the comment section below.

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2 thoughts on “5 reasons machine learning is worth learning & good career in 2022”

  1. Amazing article buddy, would highly recommend this to all my friends who are starting out in this field to give this a read. Machine learning is a form of artificial intelligence (AI) that teaches computers to think in a similar way to how humans do: Learning and improving upon past experiences. It works by exploring data and identifying patterns, and involves minimal human intervention.

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