We are expanding to offer a Bootcamp on Machine Learning Interview tactics and training. You don’t want to miss this opportunity especially if you have been preparing for interviews for Machine Learning Engineering jobs and would like to stand out from the crowd. This Bootcamp will be given by the talented Google Engineer, Osman Aka, who immerses himself in machine learning training.
We will cover several interview questions at various levels. It will be interactive and you will have a great sense of actual interview questions and different approaches for answering them. You will have guidance from our mentors and participate in mock interviews which you will receive valuable feedback from at the end of the Bootcamp. Make your first impression your best impression at every interview!
- Actual interview questions at various levels
Start learning the Machine Learning Interview Tactics with outside of business hours schedule!
The 12 hours of schedule is as follows:
October 7 – 14 – 21 – 28
Mondays, from 6:30 pm to 9:30 pm
The venue for the bootcamp is Magnimind Academy Sunnyvale Campus: 830 Stewart Dr #182, Sunnyvale, CA 94085. The capacity is limited to 20 people.
Machine Learning Interview Tactics Mini Bootcamp is now also available online. Anyone who wants to attend this mini bootcamp can join online live webinars where the same course content will be taught. Online sessions will be distributed through zoom conferences. Students will have access to the screen of the instructor, external camera showing class atmosphere, whiteboard, and be able to ask questions through chat. You may attend this mini bootcamp no matter where you are.
Early Bird: $ (between , – , )
Machine Learning Interview Tactics Mini Bootcamp has a $300 tuition fee.
For the “Early Bird” applicants (September 24 – October 4), the tuition fee is $250.
If you’re not satisfied with the course you may cancel your application.
Osman Aka works in Google Brain team in machine learning fairness field. Previously, he worked for startups in convolutional neural networks. Former math and computer olympiad students.