Movie Recommendation Website & App Using Machine Learning
https://moviesre.com/
Despre mine
I would love to help you create & deploy state-of-the-art Machine Learning models into production. I have developed & deployed the following systems end-to-end: ◦ Real-time bird detection system with 99.7% accuracy used to prevent collision between birds and wind turbine using motion detection & CNN classifier. ◦ Background removal API for various objects uploaded on an e-commerce website with 100k daily visitors using UNet. ◦ Automatic product categorization API (Flask) on images uploaded on e-commerce website with 100k daily visitors with 98.9% accuracy. ◦ Real-time people & vehicle counter with 95% accuracy using EfficientDet & DeepSort deployed on AWS which is used at 300 locations. ◦ Improved sales prediction algorithm by 35% used at 100+ locations. ◦ Licence plate detection and recognition in real-time with 99.9% accuracy using EfficientDet-D0 and Gated Recurrent Convolutional Neural Network. ◦ Comment sentiment analyzer API on a website with 10k comments daily using DistilBert. ◦ Real-time face detection (99.8%+ accuracy using FaceBoxes) & face recognition (99% accuracy using ArcFace) program with age (4.1 MAE), gender (96% accuracy) & expression classifier (67% accuracy). ◦ Tic Tac Toe bot using Q-learning on 10x10 board size (total around 9.32e+156 possible combination of moves). ◦ REST API to fetching Malaysian ID card details using Image & authenticating it. ◦ AutoML for image classification, object detection & tabular data regression/classification. ◦ Personalized movie recommendation website with 300k total movies in the database, developed using Javascript, Flask (Python), SQL/MariaDB, HTML, CSS. ◦ Improved speed of machine learning inference models on CPU by 2x using OpenVino and on GPU by 3x using FP16. ◦ Identified technical system requirements based on customer’s roadmap for 70+ clients. ◦ Analyzed complex hospital patient data (MIMIC dataset) with 1000+ features and created LightGBM/CatBoost/ XGBoost models to predict mortality & hospital length of stay. ◦ Classifying pneumothorax from X-ray images using Efficientnet-B7. Skills: ◦ Proficient: Python, Javascript, SQL, PHP, HTML, CSS ◦ Familiar: C++, Matlab, Java, Hadoop ◦ Libraries: Tensorflow, Pytorch, Keras, OpenCV, LightGBM, CatBoost, XGBoost, Flask, Jupyter notebook, Numpy, ◦ Pandas, Scikit-learn, Matplotlib, Seaborn, Transformers, Scipy, Spacy. ◦ Tools: Git, AWS, Google Cloud Platform, Photoshop, Docker
