Data Driven Recruitment Using Machine Learning

Machine learning is the most trending interdisciplinary field of study today. There are various methodology and representations to apply them to real-world situations. The areas could be vast like trending news topics, building recommendation engines, image, video and text recognition, improving cyber-security, self-driven cars and medical outcomes. The utilization of machine learning is so universal today that it is been used an umpteenth number of times by individuals, and they aren’t even aware of it. Moreover, it is considered as the best step forward towards AI.

“Machine learning has the power to synchronize volumes of data and calculations that would make it impossible for an individual person to do,” says , CTO of Promact; who is also currently a part of a team that is understanding and learning the basics of Machine Learning – Coursera with the help of the coursework taught by Andrew Ng.

The ability to identify patterns in abundance is the superpower of machine learning. Organizations that use machine learning are more susceptible to make new discoveries and find solutions to issues faster. For example, here at , there is a current team of highly skilled individuals developing a recruitment system which would help to analyze and give better results in areas of referrals, expected CTC, location, gender, age, company turnover ratio, external activity score and recommendations. With the help of machine learning and analyzing data from the previous computations, it will help in the better predictability of data-mining and statistical pattern recognition during the future process.

Some live examples that we see around the globe of machine learning are:

• The hype for the self-driven Google car
• Online purchase recommendations from sites like Amazon, Flipkart, etc.
• One of the most crucial uses in the technology world – fraud detection

With all the above means it possible to create innovative work at a faster time which is complex and would otherwise have taken up hours of work. The utilization of more advancement of machine learning will help to present more swift and reliable results.

And, the 4 points that can cover your base to machine learning are:

• Data preparation capabilities
• Basic & advanced algorithms
• Quantifiability
• Ensemble modeling

Now, most industries have large volumes of data and understand the ways and importance in which machine learning can be helpful. Hence, giving them an edge over competitors to work more efficiently and effectively.