PhyML Invos
Hi, I'm Hasham Akram, a Data Scientist and ML Engineer.
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Hi, I'm a Data Scientist and Machine Learning Engineer with a strong foundation in Physics and Data Science. My work experience
includes internships and apprenticeships in science, where I have successfully applied my skills to solve real-world problems.
My passion lies in exploring the connections between physics and AI, developing innovative models and solutions. Additionally,
My future goals include leveraging my expertise in data science, machine learning, and deep learning to drive innovation and
actionable insights in the astrophysical and celestial realm, while continuously expanding my skill set and contributing to
impactful projects. I enjoy sharing my knowledge through my blog and collaborative projects, engaging with others who share my
passion for science and technology.
an English-to-Urdu translation application built using a Sequence-to-Sequence (Seq2Seq) model with Gated Recurrent Units (GRU) and Bahdanau Attention mechanism.
This project aims to develop a Next Word Prediction Model using stacked Bidirectiona LSTMs and GRU to enhance text input efficiency and user experience.
This project implements a Region-based Convolutional Neural Network (RCNN) model for detecting wheat crops in images. The model is designed to assist in agricultural monitoring, enabling farmers and researchers to assess crop health and density efficiently.
Developed a machine learning model to detect fake news using various machine learning algorithm, achieving an accuracy of more than 92%. The system is trained on a dataset of news articles that have been preprocessed and count vectorized.
This project implements a web application for classifying chicken diseases using deep learning image recognition. The application leverages Keras' pre-trained VGG-16 model built upon TensorFlow to achieve high accuracy in disease detection.
Developed a machine learning model to predict Energy Efficiency for an Indusrial company, achieving an accuracy of 92%. The project involved data cleaning, feature engineering, model training, and deployment using Flask and Docker.
An introductory guide to PINNs, a regularization technique incorporating ODs/PDEs as physical laws for better Convergance toward optimal solution.
So, what is a physics-informed neural network? A great Source of SciML is here.
Description of the certification.
Description of the certification.
Explored astronomical technologies and scientific methods.
Developed and Deployed ML models:
Churn Prediction Model
SMS/Email Spam Classification Model
Fraudulent Transaction Prediction Model
End-to-End Structural Energy Efficiency prediction Modelling and Evaluation
Learned data analysis techniques for astronomical research.