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Fullstack build for HighRadius Dashboard during training. An AI enabled Dashboard that could predict due invoice payments' date using a XGBoost Regressor Model. The project uses Flask as a backend service to make the predictions, and Java Servlets to serve the frontend APIs.

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HighRadius Training Details

Dear Student,

Finally the wait is over! The day has come when we are about to start with the internship program. So, brace yourselves for the upcoming roller coaster ride. The starting date for the Internship is 27-Jan 2022. The tenure for the Internship will be of 11 weeks wherein you'll be responsible to build an AI Enabled Fintech B2B Invoice Management Application..

Welcome to House Dr. Strange!
Your Internship ID is (confidential)

Below are some guidelines to be followed for your journey to kick-start successfully :

  1. Software Installation for Phase-1

Here is the link to install the software Anaconda Navigator required in Phase-l. This also has an installation guide. Please follow the step by step approach as mentioned in the installation tutorial to install the software and its packages. If you’re stuck, then raise your concern in the Telegram group where your moderators can help you out on this.

  1. Notice Board
    Your Notice board link is https://sites.google.com/view/h2htechtrackgroup6.
    The Noticeboard link has been activated. Here you will find the:
  • Itinerary
  • Reading content - Video materials, reference documents
  • Scoreboard and Leaderboard
  • Important communication.
  1. Milestones and Quizzes
    The Quizzes will be self-proctored and hence you’ll be needing to keep your webcam on while appearing the same. You'll be receiving test invite mails for the quizzes. Apart from this, you will have to complete all the milestones within the tenure of the internship.

  2. Important Resources

Hope you have a pleasant journey ahead!

Regards,
HighRadius Corporation

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Fullstack build for HighRadius Dashboard during training. An AI enabled Dashboard that could predict due invoice payments' date using a XGBoost Regressor Model. The project uses Flask as a backend service to make the predictions, and Java Servlets to serve the frontend APIs.

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