Project Description

Problematic :

Without a guide plan for learning new job-related skills, individuals may struggle to determine which skills they need to learn and in what order, causing confusion and frustration. They may also lack a clear understanding of what is expected of them in terms of proficiency, leading to uncertainty and anxiety. Additionally, without a guide plan, individuals may find it difficult to stay motivated and focused on their learning journey, potentially causing them to give up on learning the new skills they need for their job.

Project presentation

Summary:

Our project aims to solve the problem of uncertainty in the user learning journey by creating a recommender system that suggests personalized courses to individuals seeking to acquire new technical skills. The system will use algorithms to analyze a learner's current skillset and learning history, and suggest courses that are relevant and aligned with their career goals. Our solution will tackle SDG 4 (Quality Education) and SDG 8 (Decent Work and Economic Growth). The added value of our project lies in its cost-effectiveness, integration, and versatility. The project will be assessed by conducting user testing and measuring the progress against measurable milestones. We plan to use a hybrid-based filtering recommendation model or a fine-tuned GPT-3 model to address the machine learning problem. We will deploy our app on both web and mobile apps.

Added values:

1. Personalized Learning

2. Time and Resource Management

3. Increased Engagement

4. Improved Learning Outcomes

5. Analytics and Insights

Setup

Here you're gonna outline:

• The libraries/packages you used (added them in the requirements.txt)
• Your developement environment (virutal env)
• Environment variables that you setup (if necessary)

Repo Structure

Data

This directory contains:

• Raw data retrieved from the various sources (scraping, collaborator, etc ...)
• Processed data: this is the output of the data preparation phase and the input for the modeling phase

Logs

For now, this is mainly going to contain simple .txt files logging your model results (parameters, train duration, evaluation, etc ...)

Models

• This repository contains your saved model that you will use to for deployment or to reproduce your results
• You can use any library of your choice (preferably save them under .pkl format)

Scripts

This folder contains any script used to:

• Retrieve data (example: scraping)
• Automate any process
• ...

Mentions

Here you can mention any outsider collaborator such as a field expert.