How I passed the Google Cloud Professional Machine Learning Engineer Certification in a week
(This is a guest post.)
The Professional Machine Learning Engineer (MLE) Certification mainly focuses on testing your high-level understanding of best practices offered by Google in machine learning projects and which Google cloud products might be the best choice given the resources constraints (time or money) or businesses problem presented in each question.
Prior to preparing for the certification, I have finished the Coursera Specialization From Data to Insights with Google Cloud Platform offered by Google. But I didn’t take any of the courses listed under the MLE certification learning path. Besides, I have basic knowledge about machine learning and the overall machine learning framework. Without preparation, I probably can get 6–8 questions correct out of the 60 questions.
My overall study strategy is to read relevant Google Cloud documentation. I started off by taking the practice exams Google provided for this certification. Next, I followed and read the google docs provided in the feedback after you submit your practice exam. In the end, it turns out mastering the following 3 google docs is most helpful. If time permits, please take the practice exam for the Professional Data Engineer Certification as well, because there is a good amount of information overlapping between these two certifications.
In addition, make sure you know the following concepts.
Basic machine learning
- Regression
- Classification
- Regularization
- AUROC
- Signs of overfitting/underfitting and how to fix them
- Training and testing split
- Different types of recommendation systems
Google Cloud products (There are lots of questions about the products and use cases)
Good luck!
