In today’s world of fast-moving science and technology, Artificial Intelligence is playing a big role in every change in every field.
From daily lives to roads, job applications to communication, Artificial Intelligence is everywhere.
While AI is the name for the large branch of science incorporated in all fields, Machine Learning is the technology behind it.
Why is there such a huge demand for Machine Learning jobs? To answer this question, we need to take a look at the technological advancements that are happening around us.
A smartwatch, email filtering, voice assistants, robotic vacuum cleaners, smart cars, chatbots, virtual health assistants – everything uses an AI-based system.
From wars to healthcare, from businesses to personal lives, AI has spread its web everywhere.
Every business wants to upgrade its older systems into AI-based ones, to ensure that they are the most advanced, cost-effective, and top-teched. Machine Learning is behind converting a system into an AI-supported one.
Given this situation, organisations are growing their dependence on AI or Machine Learning professionals. An ML professional can work in fields such as Data Science, computational linguistics, robotics.
In fact, these technical profiles are increasingly being hired by companies belonging to every industry – mining, manufacturing, banking, retail, software, IT, electronics.
Machine Learning @ BMU:
At BMU, we care to prepare our students to face the real world of business. We believe that our students should walk out of our campus with the confidence of a professional ready to take on the world.
To live our dream, we take help from industry experts, subject matter experts to design our curriculum. We have also tied up with the prestigious Imperial College of London to collaborate with us in our journey for achieving excellence for our students.
We ensure to include the best and the most relevant subjects in our courses. Therefore, we have a paper on Machine Learning and Artificial Intelligence in our Bachelor of Electronics and Communications engineering course.
We also have a paper on Data Science so that students who wish to pursue their career in this field are not deprived of this opportunity.
Let us look at some of the interesting career options for someone who has completed a Machine Learning course.
1. Data Scientist
Who is a Data Scientist?
A data scientist is a person who does number crunching and data analysis to come up with a solution for a unique problem that an organisation may be facing.
It is said that in the present day, a good data scientist is behind every successful organisation.
Here is a look at what you would certainly need to be a data scientist apart from your degree.
- Programming skills – There is no data science without programming. One needs to know to program in certain languages, which are considered the top ones for Artificial Intelligence. Such as – Python, R, Java. SQL, too, plays an important role in a data scientist’s job.
- Statistics – Data Science, as the name suggests, is nothing but number crunching using scientific methods. Therefore some knowledge and a knack for statistical methods will certainly be helpful
- Information analysis – Processed data becomes information. Therefore a data scientist must sieve through the enormous amount of data and cleanse it, inspect it and stack it into models that can be fed into the system.
A data scientist’s job is critical because he is responsible for creating an AI process that can function as a decision-making tool.
AI is not a program where the system generates a predicted output by systemically working on the input. An Artificially intelligent system mimics human intelligence by making decisions or making predictions.
This informed decision-making process is established through the data that a data scientist works on. This is why a data scientist’s role is critical to creating any AI-based platforms and even as the system works.
Sourcing huge amounts of data through voluminous stacks that are located disparately is a data scientist’s job.
He or she sifts through that data to look for information or insights that can be picked up and utilised to create the process.
It requires data scientists to find meaning in the data and decide whether it can or cannot be used in the process.
They need to look for problems and possible sources of these problems to solve them. Data scientists are required to analyse and comprehend the implications of the data and its relations with the project’s objective.
They also need to collaborate with other experts in diverse fields to achieve the goal. From an attribute perspective, these are the attributes a good scientist will require: Problem solving, Result Orientation, Critical thinking, Data analysis, Collaboration, and business aptitude.
According to Glassdoor data, a data scientist in the US can earn anywhere between 1,17,345 US dollars per year to 150,000 USD per year. In India, too, a data scientist’s average salary could be somewhere around Rs 9,52,000 per year.
2. Computational Linguist
Who is a Computational Linguist? Converting a speech to text is not an uncommon activity these days. There are many applications available online which can do that. The Translate applications on Google work on the same parameter.
It can translate a recorded speech or a human conversation. How does that happen? How does a machine read or understand a speech that is not text data?
It would not have been possible for a machine to read, comprehend and process a speech into text and then back to speech had it not been for a computational linguist.
All voice recognition software requires a computational linguist’s expertise to build them. A Computational Linguist requires very strong knowledge of programming and linguistics.
It is not only a complex and highly commendable job, but it is also a high paying one and in great demand too.
One needs to have a strong understanding of a language, its functions, grammar, syntax, pronunciation, and many other aspects to teach the same to a system.
A Computational Linguist needs to know how a human being can use a language and the nuances to incorporate the same in a computer to comprehend without human intervention.
A computational linguist needs to create rules and reproduce natural speech capability in a machine using machine learning.
Applications such as voice assistants (Siri, Alexa), Translate apps (like Google Translate), data mining, grammar checks, paraphrasing, talk to text and back apps, etc., use computational linguistics.
In the above systems, a computer or a system can identify speech patterns, understand the meaning behind the spoken language, represent the same “meaning” in another language, and continuously improve from the existing state. That’s the work of a CL. To achieve this, a Computational Linguist needs to be strong in:
- Programming: To “teach” a computer or make a machine “behave” in a particular way, an algorithm needs to be programmed. This can be done using Python or R, and a Computational Linguist need to be good at them
- Statistics and Mathematics: A Computational Linguist needs to have a natural aptitude for mathematical solutions and statistical methods.
- Linguistics: Aspects of a language – grammar, sentences, structuring, meaning, syntax, etc
- NLP: NLP or Natural Language Processing is a critical aspect of a Computational Linguist which deals with interactions between humans and computers through speech.
The average salary of a Computational Linguist can be around 81,747 US Dollars per year. They can go up to 120,000 USD per year depending on companies and even higher with experience.
Companies that hire Computational Linguists are Oracle, Google, SDL, Systran, SRI International, Verilogue, VoxGen, Intel, Facebook, Northside, Lionbridge, Multilingual, Decooda, Expert System, to name a few.
3. Human Centred Machine Learning Experts
Machine Learning is all about teaching machines to identify patterns from data and predict outcomes without being “programmed.”
In Human Centred Machine Learning, the systems combine data-driven answers with human-centric thinking models to predict outcomes.
An example of this is used in Netflix suggestions. Depending on the watchlist, it predicts and displays shows or movies that are a 98% or 95% match (an example).
Based on our watched shows, the ML system derives a pattern, combines it with human-centric thinking, and displays a prediction based outcome.
To build systems that are based on this model, HCML experts are required. These models provide a smart and personalised user experience.
Practically, all social media feeds work on the Human-centered Machine Learning model. “Based on your search results,” online retailers display additional products that a buyer has actually not searched for but may require to see.
Amazon also shows something like, along with a particular product, what did the other customers buy. These are based on HCML technology.
These are also used to detect bank fraud. In a single bank, on a single day, there are millions of transactions happening regularly.
It is not always possible to manually keep track of or detect which of these transactions could be fraudulent.
An HCML system can be designed to detect and identify patterns by combining all transactions and finding out which could be the suspicious ones.
- a) Needless to say, that to work on this technology, one needs to be strong in machine learning
- b) A strong sense of business acumen
- c) Analytical and computational abilities
An HCML expert can earn anywhere between USD 69000 to 120,000 USD per year.
4. Business Intelligence Developer
A Business Intelligence developer has a strong background in Machine Learning and Data Science based applications and develops and studies business and market trends. They work with complex data and design them into models that help a business to grow.
A Business Intelligence Developer has a very high demand in the current market where every business is ready to invest a fortune on remaining effective and efficient and above their competitors.
Given this situation, a Business Intelligence Developer can fetch a salary of around 92,000 USD in the US and 10,00,000 INR per year in India. There are no limits to how much it can go up.
A Business Intelligence developer must be from a technical background, and these are the additional skills they require:
- Strong analytical abilities, given that he or she must do a lot of data crunching using AI-based systems
- The most important skill required by a Business Intelligence Developer is their business acumen. That in itself determines a good Business Intelligence Developer from an average one.
- Excellent communication skills: They should also be able to communicate with the rest of the business units, such as the marketing team from non-technical backgrounds, about the outcomes of his analysis.
- Business Intelligence Developer must have a strong problem-solving ability and a natural knack for statistical methods
5. Machine Learning Engineer
This is the most obvious choice, and yet in this list it features at the fifth position. That’s because, If an engineer pursues a course in Machine Learning, he is bound to become an ML engineer.
But what’s the role going to look like? That’s the question. At the heart of all Machine Learning jobs lies data science and research. All Artificial Intelligence projects require Machine Learning engineers.
A machine learning engineer creates an algorithm using data that helps a system become artificially intelligent.
So what does a good machine learning expert need?
- Good programming knowledge – languages like Python, R, Scala, Java are extensively used AI, and machine learning engineers are required to program them
- Strong knowledge IDE tools- IntelliJ and Eclipse are some of the top software development IDE tools that are required to become an ML expert
- Experience with cloud applications, knowledge of neural networks, deep learning techniques, which are also ways to “teach” a system
- Strong analytical skills
INR’s average salary for a machine learning engineer could start somewhere between Rs 8,00,000 to 15,00,000 per year. It can go up to any amount depending on experience and the company’s pay scale.
There are plenty of job opportunities available in this field. Some of the high paying and highly in-demand jobs have been discussed above.
But with every passing day, newer opportunities are coming up. More and more students and professionals are making a choice of pursuing a course in machine learning.
Software engineers, IT professionals, computer scientists, and even those working in non-technical backgrounds are moving into Machine Learning to make the most of the tide.
If there is any student interested in Machine Learning but sitting on the fence trying to decide about career options in the field, hope this article will help them take the plunge.
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