Hello Friends!

We’re super excited to bring Google's latest Machine Learning offering - ‘Machine Learning Crash Course (MLCC)’ to Jordan. This course is intended for those who wish to learn about ML from a practical, applied perspective that will enable them to use machine learning within their everyday projects, and who wish to benefit from the power of TensorFlow wrapped in convenient higher-level abstractions. This is a great opportunity for professionals, entrepreneurs, and students to gain some practical experience in ML and TensorFlow.

This course will be suitable for you, if:
- You are a strong programmer
- Ideally, you are at least somewhat familiar with Python. You don't need to be an expert.
- Ideally, you know at least a little about linear algebra and calculus.

This course is not recommended for:
- Participants with extensive machine learning backgrounds.
- Participants looking to learn about the lower-level intricacies of raw TensorFlow.
Although the course does contain some basic TensorFlow exercises, most of the exercises focus on high-level Tensor APIs.
Participants seeking advanced topics in machine learning such as image conventional models and recurrent/sequential models.

The format of this course:
We will be walking you through MLCC, its modules, and basic ML concepts. Additionally, we will have you go through selected modules of the course, with a teaching assistant guiding you in course’s practical pieces/exercises and troubleshooting and pieces of the content where you are struggling. The course is open to all so you can complete the remaining modules as a self-study after the session.

Other requirements? Additionally, we’d need you to bring:
- Your laptop and charger. You will need this to go through the online course.
- Pen & paper (or any other preferable option if you plan to take notes)
A can-do attitude!

Interested?
- Register here: https://forms.gle/ipBCdWPGeeztjoVs9 by 20 April.

Selected attendees would be notified by 25 April. Filling up this form doesn’t guarantee a confirmation.

Thanks!