Original article was published on Artificial Intelligence on Medium


AI and ML

“AI is a computer system able to perform tasks that ordinarily require human intelligence. Many of these artificial intelligence systems are powered by machine learning, some of them are powered by deep learning and some of them are powered by very boring things like rules.” — DataRobot CEO Jeremy Achin.


Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.

The ideal characteristic of artificial intelligence is it’s ability to rationalize and take actions that have the best chance of achieving a specific goal.

We live in the age of AI. Artificial Intelligence has permeated our world. It surrounds us. The google assistant on your phone, Uber and Ola etc., are all AI based.

Thus, the employment demand in the field has more than doubled in the past few years so for anyone who is interested in AI, this is the perfect time to start a career.


Today, the amount of data that is generated, by both humans and machines, far outpaces humans’ ability to absorb, interpret, and make complex decisions based on that data. Artificial intelligence forms the basis for all computer learning and is the future of all complex decision making.

As business leaders and innovators race to reach the promise of artificial intelligence to deliver competitive advantage as well as cost and time savings, the technology is altering industries from finance to manufacturing with new products, processes and capabilities.


1. Fraud detection

The financial services industry uses artificial intelligence in two ways. Initial scoring of applications for credit uses AI to understand creditworthiness. More advanced AI engines are employed to monitor and detect fraudulent payment and card transactions in real time.

2. Virtual customer assistance (VCA).

Call centers use VCA to predict and respond to customer inquiries outside of human interaction. Voice recognition, coupled with simulated human dialog, is the first point of interaction in a customer service inquiry. Higher-level inquiries are redirected to a human.

3. When a person initiates dialog on a webpage via chat (chatbot), the person is often interacting with a computer running specialized AI. If the chatbot can’t interpret or address the question, a human intervenes to communicate directly with the person. These non-interpretive instances are fed into a machine-learning computation system to improve the AI application for future interactions..


A bachelor’s degree in a computer science or maths related field along with a course on Artificial Intelligence can be the perfect combination to start a career in this field.

The salary for a machine learning engineer starts from ₹332K to ₹1520K.

Since AI is such a vast field, the job possibilities are almost endless. But some common jobs are:

⮚ Machine Learning Engineer: They are responsible for creating programmes and algorithms that enable a machine to function on its own. The job is highly sought after and the educational requirements are fairly steep.

⮚ Employers look for candidates with bachelor’s degrees in fields related to mathematics and computer science. However, to progress farther, a master’s degree might be necessary.

⮚ Knowledge of Bayesian networking, proficiency with programming languages, understanding of robotics and advanced knowledge of mathematics are skills that are important to master for an aspiring machine learning engineer.

⮚ Research Scientist: These individuals work on problems in machine perception, data mining and machine learning. They are expected to be experts in multiple AI related fields.

▪ A master’s degree or even a doctoral degree is good to add to one’s resume and will improve their chances of landing a good job.

▪ Most employers look for candidates with knowledge of benchmarking and concepts such as parallel computing, machine learning etc. Extensive knowledge of machine perception is also prioritised by companies on the lookout for some professionals.


● Artificial Intelligence is a field with a lot of scope for growth. However, the technical requirements are on the higher end of the spectrum, with high paying jobs requiring master’s or more advanced degrees. For those interested in AI, this should not be an obstacle but one must be ready to put in the hours required to achieve success in their career.


Arthur Samuel, a pioneer in the field of artificial intelligence and computer gaming, coined the term “Machine Learning”. He defined machine learning as — “Field of study that gives computers the capability to learn without being explicitly programmed”.
In simple language, Machine Learning(ML) can be explained as automating and improving the learning process of computers based on their experiences without being actually programmed i.e. without any human assistance. The process starts with feeding good quality data and then training our machines(computers) by building machine learning models using the data and different algorithms. The choice of algorithms depends on what type of data do we have and what kind of task we are trying to automate.


Data is the lifeblood of all business. Data-driven decisions increasingly make the difference between keeping up with competition or falling far behind. Machine learning can be the key to unlock the value of corporate and customer data and to enact the decisions that keeps a company ahead of the competition.


Machine learning has applications in all types of industries, including manufacturing, retail, healthcare and life sciences, travel and hospitality, financial services, and energy, feedstock, and utilities. For example:

● Manufacturing: Predictive maintenance and condition monitoring

● Retail: Upselling and cross-channel marketing

● Healthcare and life sciences: Disease identification and risk satisfaction

● Travel and hospitality: Dynamic pricing

● Financial services: Risk analytics and regulation

● Energy: Energy demand and supply optimization


Machine learning engineers can take a number of different career paths. Here are a few roles in the field, and the skills they require, according to Udacity.

● Software engineer, machine learning: Computer science fundamentals and programming, and software engineering and system design

● Applied machine learning engineer: Computer science fundamentals and programming, applying machine learning algorithms and libraries

● Core machine learning engineer: Computer science fundamentals and programming, applying machine learning algorithms and libraries, data modeling, and evaluation


Machine Learning is one of the best career choices of the 21st century. It has plenty of job opportunities with a high-paying salary. Also, the future scope of Machine Learning is on its way to make a drastic changes in the world of automation. Further, there is a huge scope for Machine Learning in India. Thus, you can make a lucrative career in the field of Machine Learning to contribute to this growing digital world. In this blog, we will discuss various trends and the future scope of Machine Learning.


o Zola, Andrew. “5 Careers in Artificial Intelligence.” Springboard Blog, 5 September 2018,

o Christopher, Albert. “How to Start a Career in Artificial Intelligence in 2019? A Step by Step Guide.” Medium,13 March 2019,

o Popli, Himanshi. “Welcome to the Future. 9 Emerging Careers for Science Students.” Mindler, 19 July 2018,

o “Artificial Intelligence.” Builtin, n.d.,

o “Artificial Intelligence: What it is and Why it Matters.” SAS, n.d.,

o “Career Path: Artificial Intelligence and Robotics.” GetSmarter, 29 March 2019,




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