srikhetramohanty/Data-Science-Portfolio

This is a repository created in line with my understanding & implementation of the major complex ideas in Machine Learning & Inferential Statistics while working as a data science professional in the industry.

33
/ 100
Emerging

This resource provides comprehensive tutorials and from-scratch implementations of core machine learning and inferential statistics concepts. It offers end-to-end mini-projects demonstrating various data science algorithms, and also includes manual implementations of popular models benchmarked against industry-standard libraries. Data scientists, machine learning engineers, and advanced data analysts can use this to deepen their understanding of how these models work under the hood.

No commits in the last 6 months.

Use this if you are a data science professional or an aspiring one who wants to validate theoretical knowledge, understand the inner workings of ML models, and apply best practices in real-world scenarios.

Not ideal if you are a beginner looking for an introduction to data science concepts or if you primarily need ready-to-use libraries without delving into the underlying mathematical implementations.

machine-learning-engineering statistical-analysis data-preprocessing model-interpretability algorithm-benchmarking
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 4 / 25
Maturity 16 / 25
Community 13 / 25

How are scores calculated?

Stars

7

Forks

2

Language

Jupyter Notebook

License

MIT

Last pushed

Mar 09, 2022

Commits (30d)

0

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/srikhetramohanty/Data-Science-Portfolio"

Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.