100daysofmlcode

30-days-of-python

awesome-datascience

An awesome Data Science repository to learn and apply for real world problems.

awesome-datascience-1

:memo: An awesome Data Science repository to learn and apply for real world problems.

big-mart-sales-prediction

Sales prediction is a very common real-life problem that each company faces at least once in its lifetime. If done correctly, it can have a significant impact on the success and performance of that company.

data-science--cheat-sheet

Cheat Sheets

datacamp_project-visualisation

deep-learning-portfolio

All Things Deep Learning Projects

deepanshuvohra

My GitHub Profile

feature-engineering-techniques

federated-learning

Federated Learning is a distributed machine learning approach which enables model training on a large corpus of decentralised data. The repository tutorial for using PySyft for distributed training of Machine Learning model.

foodcouver

A statistical analysis on restaurants in Vancouver, Canada, using YELP API data

heroku-demo

holbertonschool-machine_learning

All about how to make machines learn by themselves and break the limits of imagination.

market-basket-analysis

R Projects

mckinsey-healthcare

Mckinsey Healthcare Analysis in Python

ml-mt-webapp

myprofile

neural-networks

pandas_exercises

Practice your pandas skills!

python-data-science-pipeline

Machine Learning Pipeline

python-tutorials

pythondatasciencehandbook

Python Data Science Handbook: full text in Jupyter Notebooks

scikit-learn-videos

Jupyter notebooks from the scikit-learn video series

security-and-privacy-in-v2x-communications-using-federated-learning

Advances in cellular technology are a key driver of the growing automotive Vehicle to Everything (V2X) market. In V2X communications, information from sensors and other sources travels via high-bandwidth, low-latency, high-reliability links, paving the way to fully autonomous driving and intelligent mobility. With the future adoption of 5G and beyond (5G&B) networks, V2X is likely to generate a huge volume of data, which encourages the use of edge computing and pushes the system to learn the model locally to support real-time applications. However, the edge computing paradigm raises concerns about the security and privacy of local nodes (e.g., vehicles) and the increased risk of cyberattacks. In this article, we identify open research questions, key requirements, and potential solutions to provide cyber resilience in V2X communications.

sentiment-analysis-of-amazon-reviews-using-deep-learning-in-r

split-learning-and-federated-learning

Investigating Split Learning and Federate Learning

spotify-end-to-end-aws-snowflake

twitter-sentiment-analysis

Twitter-Sentiment-Analysis-using-R

using-federated-learning-for-image-classification