there is some packages I cant use or import it outside the labs also the data used so I don’t know what is the write packages to use after takling the course.
I’m not sure this completely addresses your question, but there is an easy way to download all the files including the data that are used in any of the assignments. There is a topic about that on the MLS FAQ Thread (see topic A.2).
You can then upload everything to Colab or even install Jupyter and other packages on your own local machine. We don’t have official instructions for running locally, but here is a great thread from mentor Saif about how to run the notebooks on Colab. If you read all the way to the end of that post, Saif gives a link to another thread about running locally.
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Welcome to the community, @Hisham_Sharaf!
I guess you talk about ML Specialication, where @paulinpaloalto already wrote a great reply!
Also: I was not sure if you were asking about permission rights:
In general, you can also find some published repos -often with a license- from DL.AI with data sets like this one:
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tensorflow-1-public/C4/W4/assignment/data at main · https-deeplearning-ai/tensorflow-1-public · GitHub
E.g this one is published under the Apache2.0 license. I am definitely not a legal expert but I would rather interpret it for me personally that the data sets are integrated into the software for which the license should apply for the repo.
If you are looking for other data sets with some minimum examples how to process them w/ some effective libraries and packages, feel free to check this thread out: How to get datasets for practice? - #2 by Christian_Simonis
Best regards
Christian
It sounds lke you did not copy all the supporting files. Please have a more careful look at the thread from Saif that I linked. That covers everything, including how to download all the files and how to reference them at least from Colab. If you are doing it on your own machine, then the same general principles apply, but the “current working directory” issues may be different.
There should be a file called lab_coffee_utils.py that you got by following Saif’s procedure. Then you also need to make sure that file is in the current working directory of your notebook at the point that you execute that “import” cell. If you are not familiar with the concept of “current working directory”, then the water is pretty deep here. In that case, you may need to take some swimming lessons first before continuing here. 
If you are doing this on Colab, then Saif’s instructions do explain how to manage the currect working directory.
You can add linux shell commands in a “code” cell in a Jupyter notebook by prefixing them with ! (exclamation point or “Bang!” as it’s sometimes called). Try this:
!pwd
!ls -l
Does the “ls” output show the file lab_coffee_utils.py?