I recently completed the TensorFlow Developer Certificate in about 2 months. This certificate has been an enlightening introduction to Deep Learning for me as a Computer Science student from a tier-3 college in India.
I am deeply fascinated by Deep Learning and aspire to become a deep learning engineer. When I came across this certificate, I knew it would be the perfect opportunity to gain hands-on experience with different models and practical skills.
I am hugely appreciative to Laurence Moroney and Andrew Ng for providing these illuminating courses. It took me around 3 weeks to thoroughly understand the course material, and another 5 weeks of practice with TensorFlow to build and evaluate models for image classification, natural language processing, and time series analysis.
The main resources I relied on were the Official TensorFlow Certificate Handbook, Andrew Ng's Deep Learning course on Coursera, and parts of the book Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. Grasping the concepts from these resources along with practical experience in selecting appropriate models and layers is crucial.
The exam was fair and the above preparation gave me the foundation to score 5/5 on all 5 model assessments. Of course, the required preparation varies individually - I'm just noting the minimum that allowed me to pass.
The complete list of resources I used:
TensorFlow in Practice Specialization on Coursera
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow book
TensorFlow Certification Study Guide on GitHub
Introduction to Deep Learning for TensorFlow course
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