Which path to choose

Course Q&A AI For Everyone
1

Hi, The question I have to ask isn’t related to this particular topic. I want to know from the mentors and experienced users, which course should I go for first? Natural Language Processing specialization, GAN Specialization, or TensorFlow: Advanced Techniques Specialization? Note that I have completed the following Courses:

  1. Machine Learning Specialization
  2. Math for Machine Learning Specialization (Until what has been uploaded)

And I am currently enrolled in the following specializations:

  1. TensorFlow Developer Specialization (Currently on week 3 of Course 3: “Natural Language Processing in TensorFlow”)
  2. Deep Learning Specialization (Currently on week 1 of course 2: “Improving Deep Neural Networks: Hyperparameter tuning, regularization and optimization”)

I am also enrolled in an Electronics Engineering Bachelor’s degree program, so I am also familiar with Probability and Statistics which would be taught in the final course of M4ML Specialization

Any advice would be appreciated very much. Thank You

1 Like
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It sounds like you are already on a great path. The best plan is to finish the two that you are currently working on. Particularly make sure to go all the way through DLS through Course 5. At that point you’ve got good knowledge of a pretty wide range of techniques and then it becomes more a question of which domain areas you are most interested in. Any of those courses you list will be good things to take after the TFD and DLS specializations you mention. And all of them are independent, so you could do them in any order. There are also other things like AI for Medicine or the MLEP for more about how to deploy ML systems in real applications.

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Thanks for your input. I’m not interested in the biological side of things (although ironically, I’m working on EEG-based Emotion emotion recognition as my final year project), so I’ve decided not to go for ‘AI for medicine’. As for ML OPs, I have decided to go for it once my intermediate journey is over.