Hi Salah_hamidi,
The nice aspect of the Deep Learning Specialization you find here is that it takes you from the basic level of the AI for Everyone course straight up to the transformer architecture that forms the basis for current state-of-the-art large language models such as GPT, LLaMA, PaLM, BLOOM, Chinchilla, etc. Once you understand the transformer architecture, which is discussed in the final week of the final course of the specialization, you will be able to understand the most important aspect of such large language models as well as state-of-the-art object detection models. Your Python skills will improve steadily along the way due to the various practice assignments. If you want to be a bit more prepared before you start with the Deep Learning Specialization you can first take the Machine Learning Specialization.
Should LLMs be your main interest, then you can pursue this interest subsequently by taking the Natural Language Processing course. If you have a different focus and a practical orientation, the various TensorFlow courses will be useful to show you how to quickly build and apply models using TensorFlow. The MLOps course can teach you how to efficiently and effectively deploy such models using Google Cloud. If you prefer PyTorch and AWS and/or you have an interest in generative models, you can take the Practical Data Science specialization.
In other words, there’s much you can learn here - as I have done myself. In my case, the next step has been to dive into current literature on machine learning and transformers, whether on websites with papers such as arXiv, or in books by leading authors in the field. I have also started building a system I wanted to build for a long time, and am thinking about a start-up or cooperative venture.
As Elemento indicated, and as in any high tech field, you will never become an all-knowing expert. Things will always move very fast and you will always feel you are lagging in one way or another. Everyone has this. In my case, conceptualizing the system I am currently building has been a way not to be distracted by that. In fact, I experience it as highly stimulating that things keep moving fast and that there’s always something new to be excited about.
So, my suggestion is, start either with the Machine Learning Specialization or the Deep Learning Specialization, then find out what field of AI interests you most, and choose a next specialization that fits. Most importantly, enjoy!