Complete Machine Learning Bootcamp

Localhost: Code Collaboration with GitHub

Saturday July 11, 2020
7:00pm to Aug 11, 7:00pm IST
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Who can attend?

Locations have different requirements for who can attend. This location is open to the following:

Schedule

Starts 11th July, 2020
Every week, Tuesday, Thursday, Saturday, Sunday live on YouTube from 7:00pm IST onwards.
Duration: 4 weeks

Additional Information

Join us in this 30 days Complete Machine Learning Bootcamp series!
We have started this initiative to help students get started with Machine Learning with a focus on realworld projects.
There are no prerequisites and the course is designed in such a way that you will have fun while learning even the most complex topics in a simpler way and apply what you learn in real-world projects.
It's completely free with certification provided. We'll cover various ML topics in-depth, with projects, as well as libraries, custom implementations of algorithms, and much more!

Students will get access to live classes with doubt resolutions and get to be a part of an amazing community of developers ☀️

Register: https://codecau.se/ml30

Links & Resources

Participant Slides - Code Collaboration with GitHub

These are the slides to share with the event attendees. They are available on the registration page after they are checked in.

Unlocked when checked in.
Workshop Code - Code Collaboration with GitHub

This repo contains the source code for the workshop. The link is available on the registration page after you check in.

Unlocked when checked in.

Hosted by

Kunal Kushwaha and Ekta Mishra

Code for Cause is an initiative started by a group of like-minded people working for a similar cause. Our primary focus is to provide guidance and mentorship to students. Not only for those who lack on-campus opportunities but also for those who lack awareness about the possibilities in the field. We provide a hands-on learning experience and keep students informed about the latest trends in technology, opportunities so that they can keep up with the fast-paced digital world via following a pi-shape learning pattern.