Hello @Givin_gaming7988,
You are welcome!
I would say Yes - the ML principles are the core of these courses. The code is rather a supportive role to the core.
I think this is not a question. Instead, I consider it your very smart decision to use whatever in your hand for your future work, including treating these labs as your reference.
This specialization uses Numpy in course 1 to show how to build the simplest ML algorithm from scratch. This experience is useful for anyone who had to, well, build from scratch, like if you were a graduate student and it was for your thesis.
In a commercial setting, we almost don’t build from scratch. Instead, we use Tensorflow or Pytorch.
Starting in course 2 week 1, you will see Tensorflow in action. While they are still rather like minimal code to kick start a training, it is a good step forward. In course 2 week 3, you will come across skills on parameters tuning to tackle the bias and variance problems, which is part of the standard procedure in ML training.
In course 3 week 2, you will see a more extensive use of Tensorflow in building a very simple Recommendation model.
I would say that those labs will give you a good starting experience of making a model using industry standard libraries such as Tensorflow, however, you can’t rely on these courses to give you every detail in your way of becoming a good machine learning model trainer, partly because ML training is an art which requires experience of working on different problems. You will need to get your hands dirty 
and read more others’ code. You will discover more good libraries by reading.
For how to save the labs, since you added the Coursera tag to this post, you may refer to this one. Method 1 tells you how to save just the notebook, while method 2 the whole workspace.
Cheers,
Raymond