Training on ELK stack Training on Machine Learning and Cyber Security

> Teach multiple of schools based in London> Conduct barracuda STEM programs in England> Develop qnd deliver engaging curriculums utilizing Minecraft Education Edition, Scratch, Python, makecode, BitsBox etc> Foster a love for coding and guide students through project-based learning, nurturing problem-solving and critical thinking skills.

1. Use data systems securely to meet requirements and in line with organisational procedures and legislation including principles of Privacy by Design 2. Conducting quantitative consumer research and extracting actionable insights from consumer data 3. Creating reports to communicate actional insights for both technical and non-technical teams 4. Crafting insights through the analysis of customer sales and creating customer segmentation and audience analysis 5. Analyse data sets taking account of different data structures and database designs 6. Assess the impact on user experience and domain context on data analysis activity.

-Participated in APAO® 2023 ophthalmic equipment trade show and received TOPCON in-house training.-Successfully demonstrated medical equipment at customer locations and trade events in Karachi and Lahore.-Designed a sales funnel targeting underserved regions.-Showcased advanced LASERs from Norlase, resulting in 2 successful sales.-Provided technical guidance to prestigious clients, including Aga Khan and Liaquat National Hospital.-Delivered training on Class I devices to hospital technicians.-Collaborated with accounts and legal departments for government tenders, ensuring customer satisfaction, and analyzing market trends for business growth.

-Teaching Python programming -Making and preparing students for Data Science course

Developed the Multi Retinal Disease Classification Model (MRDCM) utilizing ensembles of EfficientNetB4 and EfficientNetV2S, achieving an AUC score of 0.973, surpassing existing literature benchmarks.Implemented various deep learning classification models, including ResNet, DenseNet, and Vision Transformers, to classify medical images.Utilized Python with Keras/TF and PyTorch frameworks to create, train, and analyze deep-learning models, with a particular focus on ophthalmic data analysis for generating insights and recommendations for medical professionals.Collaborated closely with medical professionals and researchers to identify research needs and spearheaded new project developments.Algorithm Development: Develop and optimise image processing and computer vision algorithms for object detection, recognition, modelling, and tracking in space environments. Data Analysis: Perform data analysis and validation to ensure the accuracy and reliability of vision systems. Innovation & Research: Drive innovation by researching and applying the latest advancements in machine vision and AI technologies and extracted the findings from ML Libraries (Hugging face, PyTorch, Scikit-Learn) Prototyping & Testing: Design prototyping, testing, & validation of machine vision systems in simulated environments. Documentation: Develop and maintain comprehensive documentation of system designs, algorithms, and test results. Risk Management: Identify, assess, & mitigate risks related to machine vision systems throughout development lifecycle. Continuous Improvement: Continuously improve machine vision systems through iterative testing, feedback, and enhancement cycles. Implemented various deep learning classification models, including ResNet, DenseNet, and Vision Transformers, to classify medical images. Collaborated closely with medical professionals and researchers to identify research needs and spearheaded new project developments.