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šŸ‘©ā€šŸ’» Full Stack Developer | Front End Enthusiast | Passionate about AI šŸ¤–

With a strong background in full stack development, my heart belongs to the front-end. Crafting seamless user experiences that leave a lasting impact is my forte. I'm on a mission to bridge my love for AI with my coding skills, aiming to make innovative contributions in the field.

Whether I'm coding with a team or working independently, I'm dedicated to excellence. Let's connect and explore the exciting possibilities in the tech space together! šŸŒŸ #CodeWithPassion

šŸŒ± Iā€™m currently learning ...

  • Prompt Engineering
  • Machine Learning
  • Data Science
  • How to finesse the backend using Express and React

šŸ‘Æ Iā€™m looking to collaborate on ...

  • ML and AI projects
  • React/Next projects

āš” My Skill Stack

Languages

Javascript CSS HTML5 Python Ruby TypeScript

Frameworks, Libraries & Environments

NodeJS React Vue.js Express Next JS Prisma jQuery SASS Less TailwindCSS Vuetify Rails MAMP Docker JWT ActiveRecord Pandas Google Cloud

Testing

Cypress Postman Mocha Chai Storybook Jest

Systems, CMS & Databases

Git Jira Wordpress SQL Postgres Firebase

Cross-Team Tools

Adobe Creative Cloud Slack Discord

My Latest Gists

Brought to you via my first GitHub Actions Workflow:
  • index.tsx

    Code sample: React Transfer List using TypeScript

  • wrapper_methods.ipynb

    Project: Feature Engineering Wrapper Methods / Codecademy ML AI Engineering Career Path

  • tennis_ace_challenge.py

    Create a linear regression model that predicts the outcome for a tennis player based on their playing habits. By analyzing and modeling the Association of Tennis Professionals (ATP) data, you will determine what it takes to be one of the best tennis players in the world.

  • ProductDefects.ipynb

    Exercise in probability distribution from Codecademy Machine Learning and AI Foundations course

  • Codecademy_NBA_Trends_Project.ipynb

    Analyze National Basketball Association (NBA) data to look at associations between teams, win-rates, playoff appearances, and more.

  • This is Jeopardy.ipynb

    Data analyzation project from Codecademy ML/AI Foundations course

Connect with me on:

LinkedIn Upwork

Lauren Ashley's Projects

city-timezones icon city-timezones

A light and fast method of looking up timezones given the name of city.

ml-random-forests icon ml-random-forests

Ensemble methods project via Codecademy. By using this census data with a random forest, we will try to predict whether or not a person makes more than $50,000.

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