More than four years of experience in Machine Learning with Python. Sound exposure to analyzing different types of data like continuous, categorical, text, and images. Expertise in Data Collection, Data Preprocessing, Data Analysis techniques like Data Visualization, Feature Engineering, and Statistical Analysis; and model designing with Machine and Deep Learning algorithms.
In AI, you have to update or equip yourself from time to time with advanced techniques. That’s why I have not only completed more than sixty certifications at Coursera but further studying AI in Medicine and PostgreSQL. My major certifications are in Python Programming, Applied Data Science with Python, Statistics, Deep Learning, Natural Language Processing, and TensorFlow. Recently exploring text data with the help of Transformer models and image data like medical imaging with models like U-Net and Resnet. In some projects during certifications, SQL is widely utilized.
Being an engineer, I would like curiosity, which I believe, helps me to complete the task at any cost, in other words, "never give up". This technique always keeps me on my toes to compete with others and hence grow further, the sky is the limit. Working with data-associated challenges will definitely provide a further path towards enhancing my skills and knowledge, as well as learning new techniques, and hence exploring new dimensions of Artificial Intelligence.
Main PROJECTS Natural Language Processing
1. Transformer For Text Classification, Named Entity Recognition, Question Answering, etc
2. Glove, Fast Text Word Embedding on different NLP corpus
3. N-gram language model for an AutoComplete prototype system
4. Post tagging through transition and emission matrix in Hidden Markov Model and Viterbi algorithm
5. Siamese Network through Trax for finding Question Duplicates
Computer Vision
1. Multi-class segmentation model to identify segment tumor regions in brain using MRI data
2. Emojis Prediction through Random Forest & Support Vector Machines
3. Transfer learning with MobileNetV2 on a pre-trained ImageNet for Alpaca Animal Prediction
4. Object detection on an Autonomous driving Car system through YOLO
5. Detecting infection in maize plant due to fall armyworm through transfer learning, Inception
6. U-Net and Mask-RCNN to identify and detect numbers, pets, zombies, and more
7. Exploring image segmentation, object localization, and object detection through transfer learning.
8. Image Search Application with OCR, Open CV, and Tesseract
AI in other fields
1. House Price Prediction with Improved ML Techniques
2. Statistical Analysis with Python using National Health & Nutrition Examination Survey Data
3. Loan Predictions with Deep Learning
4 Clustering Model with Foursquare API
5. Energy Consumption in Netherland, a Nonlinear Regression Analysis
6. Accident Risk Places, an Analysis of US Traffic Data
7. Jane Street Market Prediction, A Kaggle Competition
Portfolio: kaggle.com/hark99