Difference between revisions of "Watch me Build a Cybersecurity Startup"
(→DharmaSecurity) |
|||
| Line 25: | Line 25: | ||
* [[Python#Flask |Flask]] microframework for Python | * [[Python#Flask |Flask]] microframework for Python | ||
* [[AWS Lambda & Python]] and [[AWS with TensorFlow]] | * [[AWS Lambda & Python]] and [[AWS with TensorFlow]] | ||
| + | * [[Cybersecurity]] | ||
== [http://aws-web-analytics-dashboard.s3-website-us-east-1.amazonaws.com/ DharmaSecurity] == | == [http://aws-web-analytics-dashboard.s3-website-us-east-1.amazonaws.com/ DharmaSecurity] == | ||
Revision as of 09:45, 2 September 2019
YouTube search... ...Google search
- How do I leverage AI?
- Watch me Build a Marketing Startup | Siraj Raval
- Watch me Build a Healthcare Startup | Siraj Raval
- Watch me Build a Finance Startup | Siraj Raval
- Watch me Build a Retail Startup | Siraj Raval
- Watch me Build a Trading Bot | Siraj Raval
- DharmaSecurity Demo
- OpenMined
- Analytics Dashboard React Application
- Node.js - JavaScript runtime built on Chrome's V8 JavaScript engine
- React = JavaScript library for building user interfaces
- Create React App | GitHub
- Flask microframework for Python
- AWS Lambda & Python and AWS with TensorFlow
- Cybersecurity
DharmaSecurity
I've built a demo app called DharmaSecurity, a fraud detection tool for businesses. The way it works is that once signed up, a business will paste a code snippet into their website, and then they'll get access to a dashboard that tells them how many fraudulent accounts they have. Our app will use machine learning to automatically remove suspected fraud accounts, and flag likely ones for review. To build this, I use a suite of AWS tools, Python, Javascript, a Logistic Regression (LR) model, a credit card fraud dataset, and a library called OpenMined to enable federated learning and secure multiparty computation. I've packed a lot into this video, animations, code, music, screencasts, skits, etc. Enjoy! Code for "a Cybersecurity Startup" | Siraj Raval - GitHub