I am Jashandeep Kaur, a Computing Science graduate from Simon Fraser University with a specialization in Artificial Intelligence (AI), Machine Learning, and Data Analysis. My academic journey involved completing courses such as Artificial Intelligence Survey, Computational Data Science, Special Topics in AI, and a Research Project in AI, which gave me a comprehensive understanding of AI concepts and their real-world applications.
My skills include: data analysis, visualization, and machine learning. I have experience in data cleaning and transformation using FME (Feature Manipulation Engine), which has been essential for handling complex datasets. Additionally, I have a strong command of tools like Power BI for creating insightful and interactive dashboards, enabling data-driven decision-making. My experience spans from preparing, transforming, and analyzing large datasets to effectively communicating insights through visualizations that are easy to comprehend.
In terms of technical expertise, I am skilled in languages like Python, Java, and C++, and have developed a wide array of machine learning models, ranging from image classification to multi-label text classification. My professional experience includes roles such as an AI Engineer/ Machine Learning Engineer and Solutions Designer and Developer, where I researched and developed geo-spatial time series and multi-text classification models. These experiences involved both data-centric and human-centric approaches, integrating advanced analytics with user-friendly solutions.
With a passion for solving real-world problems using AI and data science, I thrive in environments where I can apply my analytical skills, develop intelligent systems, and visualize complex data patterns. In my free time, I enjoy playing table tennis, reading books, and staying active at the gym.
This project involves building a model to classify over 1,000 images of cats and dogs, which were scraped from Google Images. The goal is to accurately distinguish between cats and dogs in images.
The Dogs & Cats classification task is a foundational problem for learning how to develop, evaluate, and use convolutional neural networks (CNNs) for image classification
In this project, analysis and prediction of product categories and prices for high-end luxury brands like Gucci, Louis Vuitton, and Loro Piana across Canada, the USA, and the UK was conducted. The goal was to develop machine learning models that can help new brands in the future estimate the price range and predict the appropriate category for their products.
This project involves predicting the name of a country based on various factors such as population, elevation, year, unemployment rate, latitude, longitude, and time zone. By analyzing these features, a classification model was developed to identify countries accurately using data-driven insights. The model leverages geospatial and socioeconomic data, showcasing the potential of machine learning in country-level predictions.
I have gained proficiency in Python through various university courses such as Computational Data Science, Special Topics in Artifical Intelligence and practical work experiences.
I have gained proficiency in power BI through practical work experiences at my workplaces by working on number of projects
I have gained proficiency in SQL through various university courses such as Dataabase systems and practical work experiences.
I have completed FME courses on Safe Software’s website, gaining expertise in FME. I have successfully applied these concepts to multiple projects at my workplace
Link to course or bootcamp