How to prepare for the TensorFlow Developer exam
- The TensorFlow Developer Certification Handbook
Your adventure should begin here. The topics included in the exam are listed in the manual. I advise giving it a few readings. Although the topics may seem difficult, the resources listed below will totally cover everything you need to know. It merely helps to be aware of the information you really must possess in order to pass.
2. TensorFlow in Practice Specialization on Coursera
The training taught by Andrew Ng and Laurence Moroney was fantastic.
It is something that anyone interested in machine learning should definitely consider. In just a few weeks, the course covers every topic, moving from a fundamental model through time series of data to image and text categorization. The most crucial exam resource is this (and getting started with TensorFlow Course in general).
3. Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow 2nd Edition
I once again heartily recommend this book, which I already mentioned in my last blog article about learning machine learning. This book’s 700+ pages essentially cover every aspect of machine learning, including those subjects that aren’t included in the test. However, it’s essential reading for anyone who wants to lay a strong foundation for a career in machine learning, not merely to pass a test.
4. Introduction to Deep Learning by MIT
Excellent deep learning course from a top-tier university that is also free! The most important courses for the exam are the first three, which cover deep learning in general, convolutional neural networks (often used for computer vision), and recurrent neural networks (typically used for text processing).
5. A PyCharm Tutorial
The exam is conducted using PyCharm (a Python IDE). Before the exam, I’d advise getting familiar with it and practising with a few example models to make sure everything is operating well in your environment.