Looking for insights in data, both in the physical and digital world. Leveraging stats and machine learning concepts within the domain of computer networks.
Powered by :< Observation >, < Statistics >, < Machine Learning >.
Always learning, thinking, and questioning.
I believe that everything that sustains is just an algorithm at its best implementation.
On a lighter note Blackholes fascinate me too.
An IBM Certified Advanced Data Scientist | GCP | Kubernetes | Networking |
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Experience
Working towards build Data Science pipelines and implementation, assessment of the need for ML in Network problems.
Won the annual company Hackathon for an ML based solution for malicious URL and network endpoint scoring.
Specific domain of ML currently working on would be Time Series analysis and format of predictive Analytics.
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Projects
Producing smart configurations for assembly line workstation systems to self-adapt to variations in product design.
Models:
[GANs, DBSCAN]
application that identifies an emergency and aims to narrow down on the possibilities just by the witnesses description (what she/he thinks is happening during the emergency )
Models:
[skip-gram model, Bi-LSTMs]
AI based smart listener which can recognise the speech text only based on lip movement. This solution makes use of Deep Learning models in Computer Vision.
Models used :
[LSTMs]