Machine Learning

7 Reasons why learning Machine Learning Will Boost Your Career!

March 7, 2020

In keeping with the current trends where the world is rooting for AI systems, if you catch yourself wondering if machine learning should be the next step in your career, you are in the right place.

In the next five years, Artificial Intelligence is set to grow exponentially. On one hand, this growth will trigger a phase of higher expectations and innovative solutions. On the other hand, it will further deepen the gap between demand and the availability of trained personnel, particularly when it involves people who have made a career out of being deft at machine learning.

Quick Recap: Machine learning is a core subset of Artificial intelligence. ML is mostly programming and algorithmic work to improve the efficiency, capacity, and performance of AI systems. ML can be aptly described as the process of making the machine learn and understand what to learn and what to not.

See also: Future of Machine Learning in 10 years

So, here are the 7 reasons you need to get convinced that Machine Learning could be the next big thing in your career:

Reason 1

Everyone is going to be using it!

Machine learning has carved itself a niche. Almost every imaginable field has a use for machine learning and has created a career for it. For example:

Financial services: 

The financial industry uses machine learning for two major jobs: to identify important insights in data, and to prevent fraud. These insights help investors in identifying investment opportunities and in knowing when to trade. Also, it helps the companies in segregating potential investors based on their behaviour data. Using the same behaviour data, one can detect early signs of fraud.


Owing to the advent of wearable devices and sensors, real-time data about a patient's health is available and this can be used to assess the patient's health in real-time. Analysing such accumulated data helps the medical experts recognise such trends or red flags which may lead to improved diagnosis and treatment. 

Marketing and sales

That 'you might also like' tab on your shopping websites is machine learning at work. ML algorithms analyse your shopping history and predict and promote items you might also like. This gives us a personalised shopping experience and saves us both time and money.


Government agencies have multiple sources of data which can be mined for insights. For example, one can help detect fraud and minimise identity theft using machine learning on the Aadhar Card data (UIDAI scheme).

Oil and gas

ML helps in finding new energy sources, analysing minerals in the ground, predicting refinery sensor failure, calibrating oil distribution to make it more efficient and cost-effective. The vast number of ML applications in this field is still expanding. 


the transport industry relies on making routes more efficient and predicting potential problems and hence analysing data to identify patterns and trends in this regard is the key to increasing profitability. ML’s capacity of data analysis and modelling is particularly important to public transportation, delivery companies etc.

The list of fields implementing ML is not limited to the ones mentioned above, these are just snippets. All these applications indicate the trend that ML is soon to be the field on a boom and a lot of research work will go into it. Learning ML while it is on the rising part of the curve before plateauing to a mature stage would be a great decision right about now.

Reason 2

Jobs will find you

Taking in consideration the exponential multiplication of processing power every five years, IFTF experts have estimated that 85% of future jobs haven't been invented yet. These jobs will not be the regular run-of-the-mill lot. These jobs will make extensive use of AI and even more so, of ML. 

As we discussed earlier in the article, the number of organisations embracing AI and ML is going up by the second. In such a competitive scenario, engineers who are adept in machine learning, will be highly coveted, with a workload to match. 

During such time when a technology is experiencing a boom, experts are in low numbers and people who start their careers in the technology at this point, become its flag-bearers.

The focus of ML is gradually shifting towards more complex real-life modelling and the number of parameters in each situation will go up in a spiral. The talent-demand gap is steep and this, in general, works in favour of the employee rather than the employer. 

If you start polishing your ML skills today, you will have a plethora of opportunities to choose from in the next 5 years. However, It is advisable that you stick to a job for at least two years to garner relevant experience in your career. 

Reason 3

Best time to be a start-up

For those of us who do not see a job or an employment as their ultimate goal, ML has a lot to offer. 

With the advancement in computational power, the limit on what we can do with computers has relaxed. As better hardware keeps coming up, we will have more flexibility. This higher flexibility will team up with the tools of today like R, Python, Java APIs, Julia to open up a whole new world of possibilities. 

To stay competitive in the changing times, a company needs to be agile. According to a research by Dell Tech, 78% of businesses feel threatened by digital startups and are right to take that stand. For example, AirBnB started out in 2008 and today has more rooms than the Hilton chain, the Marriott and the International combined, in terms of total rooms available online, not just real estate.

Moreover, in the same study, almost 48% global businesses don’t know what their industry will look like in three more years. Around six in ten businesses are unable to meet the client’s top demands. 

Fresh ideas and innovative solutions for persistent problems are bound to come up. Especially with the demand-supply gap between businesses and clients, somebody will have to fill up the gap. 

If you have an idea that you believe you can sell, start working on it. Not like a weekend DIY project but pour all your ML skills into it and package the service so that it pops up.

Reason 4

‘Upscale’ your career while the world is upscaling!

73% of these global companies believe that digital transformation could be even more widespread than it is now. (IFTF report)

This is a statement which needs no proofs. We all know it and we are watching it happen. What we are hinting at right now, is how it will affect your career.

When digital transformation becomes that prevalent, AI will be everywhere. Multiple algorithms and systems will function side by side. Preventing their interference with each other and helping them collaborate with each other will be a whole different task. Engineers will also need to write better algorithms to help intelligent systems interact with each other in unforeseen situations. 

In 2011, Cornell Creative Machine Lab scientist tried to make two chatbots have a conversation with each other. It got pretty sassy and the conversation did not last for more than five minutes. These chatbots were advanced enough to have a successful conversation with humans but with another chatbot, misunderstood meaning led to a virtual standoffish conversation. 

In future, we would need a more seamless approach and subsequently more seamless algorithms i.e. Algorithms scalable in real-life situations. 

As an ML engineer, you will be writing these complex algorithms. You will need to constantly improve your logic and help the algorithm consistently perform well. In other words, your skills will be relevant for a long time and overtime, they will also be refined.

Reason 5

Being in is the only way out!

AI is expected to steal many professions like telemarketers, proofreaders, computer support specialists, marketing and sales analysts etc. AI has been performing quite well in these jobs and that’s why these will be completely automated.

Similarly, as the machines get smarter, they will take up more jobs in the name of holy automation. However, we will still need humans to train, test, improve and maintain these machines. We will also need humans to manage humans working in these domains. 

It’s very much like saying that if you wish to ensure that you have a job when the kingdom comes, you would be better off working the revolution.

So you and your HRM will sit pretty for a long while.  

Reason 6

No Experience? No issues!

There is a huge gap in the number of ML engineers required and the number of engineers available. This has forced organisations to develop their own workforce from the scratch. People who have good maths skills and decent coding knowledge of major programming languages like Python, R or Java, have a good chance of making it into the foyer. It’s a plus if your algorithm game is strong. The remuneration will be, however, lower in the beginning but once you’ve gotten hands-on experience of a project or two, it will only go up from there. 

Reason 7

It’s not even that hard!

Machine learning is all about seeing a pattern, a web of correlations when there are none in the first sight. Then, lessons from this unstructured data are applied to unseen data and the system is expected to do so with minimum error. 

Making the machine learn what it should and ensuring that the margin of error stays minimum is the job of an ML engineer. 

You have a fair understanding of the job, you know the potential ML holds in the close future and what all benefits you are looking at if you ride this tide. 

The most important factor of all, however, is your passion for learning ML. If you have an interest in this procedure, if all the possibilities that can be realised with the help of ML kindle your imagination and you are willing to put in the necessary hard work, then you have your work cut out for you. Learning ML makes more sense if you are interested in the concept and either are a decent programmer or are working towards amping your programming up.

There are certain prerequisites in terms of skill for learning ML- you should be able to write a reasonably non-trivial program, be aware of linear algebra, probability theory, graph theory, calculus, statistics, graph theory and optimisation methods. 

The two most popular languages used for ML are R and Python, chosen depending on the end-function of the intended program/algorithm.

ML in itself is an iterative method, implementing the concepts mentioned above. It can be easily automated (given the iterative nature) and that too would be on you as to how you automate it. 

It might seem like a lot of labor, moving from your current profile to that of a machine learning engineer. Considering the prospects and integrating them with your interest in the technology, will give you a good answer of where you should be. If you decide in favour of opting for machine learning in your career, you’ll be pleasantly surprised that once you understand the concept, ML is actually very interesting.


In this article, we discussed the current and upcoming trends in machine learning and how they could affect your career if you choose to shift your focus from your current job to machine learning. If you are not a software developer or someone from a coding background, you can still become a machine engineer. However, it will be harder than it is for people from the development arena. You’ll have to start from the bottom, learning C/C++, Python/R and mathematics concepts like linear algebra, probability theory, statistics and analytical studies etc. It might be an uphill journey but ask any ML developer, the career prospects it opens up for you are entirely worth it.