![Photo of Sahil Kakkar](/_next/image?url=https%3A%2F%2Fprod-outdefine-resume.s3.amazonaws.com%2F9f224b17-f918-48ae-91f9-cdd70a1750bb.jpeg&w=320&q=100)
Sahil Kakkar
Machine Learning Engineer
I am an enthusiastic and results-oriented AI enthusiast currently enrolled in a Master's program in Artificial Intelligence at Yeshiva University. My journey into the realm of AI commenced with intensive coursework, including completion of a Machine Learning course from Stanford University, the Deep Learning Specialization from DeepLearning.ai, and an Advanced Certificate Programme in Data Science from IIIT Bangalore. These educational endeavors have furnished me with a robust theoretical foundation and practical expertise in Python, PyTorch, fast.ai, scikit-learn, statsmodels, NumPy, Pandas, SQL, Matplotlib, and Seaborn. My hands-on experience has been enriched through various projects and internships. Notably, one of my projects achieved acclaim, securing the 3rd prize at my college. Furthermore, I had the privilege of interning at IIIT Hyderabad, engaging in cutting-edge AI research. I am eager to connect and explore potential collaborations on exciting AI projects or delve into discussions regarding the latest advancements in artificial intelligence and machine learning.
Careers
Research Intern
iHub Data
- Worked on "Retinal Developmental Disorder Detection"
- Implemented the state of the art research paper
- Accuracies achieved on OCTID Dataset were 0.99 (AP) and 0.97 (AUC)
Data Science Fellow
Fellowship.ai
- Taught KNN, K-Means, PCA, GBM in the Fellowship.ai's ML course
- Synthesised a dataset and implemented a Multi-label classifier
- The final accuracy on the validation set was 99%
Education
Yeshiva University
Artificial Intelligence
Maharaja Agrasen Institute of Technology
Computer Science and Engineering
My projects
Impostor Detection using Behavioural Biometrics
No image available
- Identified users from their mouse movements by employing intense feature engineering with sophisticated anomaly detection - Trained a unimodal parametric model using Weibull distribution based on a research paper
Role: Feature Engineering and Model Training
Completed: 2/2022
Live project: https://github.com/Sahil1776/Impostor-Detection-using-Behavioural-Biometrics
- 1
- 2
Welcome to Outdefine
A free tokenized community dedicated to connecting global tech talent with remote job opportunities. Our platform is designed to help you connect, learn, and earn in the tech industry while providing the chance to collect DEF tokens. Join our vibrant community today and explore a world of possibilities for your tech career!
Join for free