Understanding Machine Learning
In the early age of science when some researchers were interested in having machines that could learn from data, Arthur Samuel in 1959 coined the term Machine Learning and built a program that uses past data as inputs to predict new outputs. So your program becomes good with time at offering you with maximum success rate without being explicitly programmed to do so.
The intelligent subset of Machine Learning is a part of Artificial intelligence that classifies data based on models that have been developed and to predict future outcomes based on these models. To understand it better let’s dig deeper about Machine Learning.
What is Machine Learning (ML)?
We as a human being have an ability to learn from our past experiences and Machine Learning is the most exciting technology that offers your computer to be more like humans. With the ML algorithm, your computer automates its learning and improving process without being actually programmed or you can say without any human assistance. Your computer starts to predict better but it all starts with feeding your machine with good quality data and then training based on machine learning models. These models are created using historical data and different algorithms depending on the task we are trying our machine to achieve automatically.
The most common use cases of Machine Learning (ML) are Recommendation engines like those on streaming platforms, digital assistants like Siri, malware threat detection, fraud detection, spam filtering, business process automation, and predictive maintenance. Today Machine Learning is facilitating more domains we could think of. Researchers, data scientists, engineers, and analysts use ML to produce reliable, repeatable decisions and results along with discovering trends in data sets.
Machine Learning is offering a lot of career opportunities in India to the freshers and experienced professionals with sound knowledge of Statistics and probability, Data Evaluation and Modelling, ML algorithms, Signal processing techniques, and Programming Languages. And professionals with such skills can unlock a great career height for themselves in India and all over the world.
Scope of Machine Learning in India
Since Machine learning is everywhere, its scope in India, as well as all around the world in various sectors including healthcare, retail, financial services, transportation, is promptly
increasing. Today ML is not limited to the investment sector but also allows organizations to make effective business strategies as per the predictions of the Machine Learning algorithms.
So, let’s discuss the scope of Machine Learning in various sectors and their salary trends depending on factors like Company, Experience, Location, and skills.
Job opportunities outpacing the number of skilled ML Engineers:
The demand for ML professionals in India like ML Engineers, Data Scientists, Data Architect, and Data Analyst is too high then the number of skilled engineers in comparison to other career fields. In fact, according to Gartner, the world’s leading research and advisory company suggests that there will be 2.3 million jobs in the field of AI and ML by 2022.
Highest Salary Package:
In comparison to other job profiles, Machine Learning Engineers are offered better salary packages as of their higher demand and limited availability in the market. On average, freshers with Machine Learning skills are offered up to 6LPA to 6.8 LPA packages in India including bonus and profit share. Similarly, experienced ML Professionals’ salary ranges from 11LPA to 11.4LPA and above as per their relevant working experience.
Steady career growth:
Back in 2016, when there were only $1.07 billion jobs stated by TMR, now this number is predicted to reach up to $19.9 billion by the end of 2025. So, if you are looking for year-on-year career growth then being a Machine Learning professional is the right career choice for you.
Machine learning opens the door to advanced career opportunities:
Organizations today are looking for digital transformation which requires ML engineers and Data Scientists to work together. Working with Data Scientists helps ML engineers to have a broader set of knowledge and skills and they can finally work on bigger projects and products on their own.
Being a Machine Learning Engineer you can quickly and automatically produce models and help your organization to analyze bigger, more complex data and deliver faster, more accurate results. You can be a valuable asset to your company helping them identify profitable opportunities and avoiding unknown risks along with building a full of opportunities for yourself.