Learning Path Index

A repo with data files, assets and code supporting and powering the Learning Path Index.

Table of content

• Overview
• Key Features
• Potential Innovations
• How to contribute to this initiative?
• Important Links
• Credits

Overview

The Learning Path Index is a dynamic and versatile repository designed to empower learners in the fields of Data Science and Machine Learning. It offers a curated collection of byte-sized courses and learning materials, meticulously organized and tagged to facilitate effortless discovery. Whether you're a novice or a seasoned practitioner, the Learning Path Index is your gateway to knowledge, tailored to your interests and needs.

The outcome of this effort was the creation of this Git repo and the KaggleX Learning Path Index Dataset by the mentors and mentees of Cohort 3 KaggleX BIPOC Mentorship Program (between August 2023 and November 2023), see the Credits section for more details.

Key Features

1. Comprehensive Collection

• A vast array of byte-sized courses and learning materials covering Data Science and Machine Learning topics.
• Courses are categorized and tagged by keywords, categories, topics, and interests, all closely aligned with the world of Data Science and Machine Learning.

2. Robust Search and Filtering

• Effortless search and filtering capabilities allow you to find the content you need quickly.
• Search by full or partial text, including keywords, categories, topics, and interests.

3. Collaborative Contribution

• Easy-to-use mechanisms for adding new courses and enhancing existing entries.
• Contribute your expertise and help refine course definitions for the benefit of the entire community.

4. Automated Data/Course Scraping (WIP):

• Automatically scrape course information and details from multiple platforms.
• Data enrichment and augmentation using AI!

5. Keyword Extraction with KeyBERT and WordWise-Kaggle Notebook

6. Learning Pathway Index Data Cleaning and Preprocessing

7. Contextual Search On Kaggle Learning Path Index

Getting Started and Setup

Please refer to the getting started guide, Getting Started, for setup instructions.

Potential Innovations

Explore exciting possibilities for enhancing the Learning Path Index:

1. Course Chunking: Divide pending courses into byte-sized modules for a more digestible learning experience.

2. Content Enrichment: Assist in fine-tuning, correcting, and enriching existing byte-sized entries to ensure high-quality learning materials.

3. Kaggle Dataset: Transform the Learning Path Index into a dataset and host it on Kaggle Datasets for broader accessibility.

4. Keyword Extraction: Automatically extract keywords from course websites and byte-sized modules to enhance search functionality.

5. Exploratory Data Analysis (EDA): Conduct exploratory data analysis on course materials to gain valuable insights into the content of the datasets.

6. NLP Profiler: Implement NLP Profiler and Pandas Profiler to analyze courses by various parameters, uncovering hidden patterns.

7. Interactive Learning: Develop a Streamlit, Shiny, or Mercury app to make these courses available online, fostering an interactive learning environment.

8. Cloud Hosting: Deploy the app on popular cloud platforms like Heroku, Netlify, AWS, or others for widespread accessibility.

9. Notebook Integration: Create Google Colab, Kaggle Notebook, Amazon Notebook, or Interactive Jupyter Notebook integrations to facilitate seamless course exploration.

10. NLP Enhancement: Apply advanced NLP techniques to the existing data to extract deeper linguistic value and meaning.

11. Generative AI: Utilize the dataset to build Language Model (LLM) and Generative AI models, opening doors to innovative AI-related activities.

12. Continuous Improvement: Brainstorm and implement additional ideas to enhance the tool's utility for both the community and individuals.

Join us in this exciting journey of learning, collaboration, and innovation. Together, we can create a valuable resource for the Data Science and Machine Learning community. Let's embark on the path to knowledge and discovery!

How to contribute to this initiative?

• You can also join us by taking part in the next KaggleX BIPOC Mentorship program (also see this)
• Keep your eyes open on the Kaggle Discussions page and other KaggleX social media channels. Or find us on the Kaggle Discord channel to learn more about the next steps
• Create notebooks from this data
• Create supplementary or complementary data for or from this dataset
• Submit corrections/enhancements or anything else to help improve this dataset so it has a wider use and purpose

Important Links

• KaggleX BIPOC Mentorship program (also see this)
• KaggleX Learning Path Index Dataset
• KaggleX Learning Path Index GitHub Repo
• New Official Kaggle Discord Server!

Credits

Credits for all the work done to create this Git Repo and the KaggleX Learning Path Index Dataset goes to these mentors and mentees (in no particular order): Manish Kumar, Ben Aji (mentor), Emmanuel Katchy, Ezeogu Ernest, Manish, Mustafa, Nnamdi Idowu-Anifowoshe, Sheba Alkali, Zainab ikeoluwa, Wendy Mak (mentor), Misirya Hameed (mentor), Chukwuebuka Obi, Victor Umunna, Pui Yueng, Afolake Solomon, Faith Osoro, Chukwudi Idowu and many others who were part of the Cohort 3 KaggleX BIPOC Mentorship Program (between August 2023 and November 2023).

Our gratitude also goes to our silent supporters of this initiative from organisers to the mentors and mentees whose help and support kept us going.

Note: In case your name or mention is missed out in the above list, then please let us know.