Python Prerequisites For The Entire Stanford ML Course

Hello all,

I am Menelaos Gkikas from Greece. I’ve graduated from Course 1 here, but it seems that the Python experience advances in the coming courses (ML Specialization) and that’s why I was consulted to study Python before any more progress.

DataCamp as you may know is one of the excellent sources of studying Python. I have completed the 4th Statements of Accomplishment in Python Fundamentals before a hands-on project and the final assessment.

I need your advice in terms of how deep should I go before I come back to Stanford. DataCamp offers numerous of exciting fields, we can cancel subscription in order not to pay if we need more time but it seems that I could be lost in endless Python Materials and lose valuable time.

Python Fundamentals is here:
Machine Learning Fundamentals is here:

Do you believe the above are enough in terms of studying your course?

Importing and Cleaning Data is here:
But the last one refers to data-entry and not importing libraries as it happens with your code. Is it needed?

Bear also in mind that Stanford’s codes are somewhat multidisciplinary and advanced. There may be extra commands and models not having been studied previously. Will I be able to cope with them with my intuition or does this seem a dead-end? Are extra commands gonna cause me problems?

Furthermore, in terms of becoming familiar with Python there are also classes in DataCamp relevant with DeepLearning and NLP. Do we all agree this is extra material not requested especially if I have not studied the science behind code writing?

So far I’m looking for your advice in terms of whether Python Fundamentals and ML in DataCamp are enough to come back here as it’s also true this will take me quite a few more months and I wanna optimize my time-frames in terms of adding more skills in my resume.

Will I be able to handle Stanford’s situations and with which of the above skills? Should you search DataCamp to answer this and is this a good source of studying Python? They have excellent profile nonetheless.

Looking forward for your lights

Thanks in advance!

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You can copy & paste the links in your browser because for some reason I can’t open them.

Hello M! That is an interesting post. Let me answer it with my experience.

I am not an expert in Python. I just learned the basics and then start learning more by doing. Never wait to become a Python expert and then start machine learning journey. I just did the first and second courses of Python for Everybody and then start my ML journey.
Also, try to read the utils files of all the assignments. And if you don’t understand any code, ask GPT about it.



Hello saifkhanengr,

Without any mean of offense our courses descriptions seem identical as far as Python Fundamentals in DataCamp is concerned even though I can’t find out in your course what exactly you know. But you almost spent 4 months if not longer and this is what I’m planning to do with the Machine Learning Course in DataCamp as well.

DataCamp is much more analytical at its descriptions and as you may have the experience of, I keep dozens of notes in my “notebook” adding to the equation concetration and my whiteboard.

The reason though I reply now is because I see again and remember differences in syntaxes and commands. I’ve completed 1/2 the way, each institution has its own plan of study (syllabus) and I don’t know what will happen in here if there are adding ups, subtractions or differences in the code.

How do you deal with these?

Looking forward for your lights


It is your choice which course to take. All the courses are the best.
Good luck.


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Thank you for sharing your links and experience here as I’m sure they will serve many readers.

One common trait I found in successful entrepreneurs and computer geeks is that they’re problem solvers and most of all they’re doers.

Why not forget about your fears, get started and adapt your plan if needed? Iteration is key, fast iteration is the secret of success.

Of course, this is just my opinion and I may be wrong.

Personally speaking Philippe,

I’ve blocked further progress in Coursera (graduated from Course 1 in Stanford but not Course 2 and Course 3) and I’m investigating to do the same in DataCamp (plenty of revision material before moving on) for I do not want to pay money without exploiting the platforms… There is that option.

just a clarification - DLAI doesn’t offer “Stanford courses”.
DeepLearning.AI and Stanford University are separate entities.

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Hello TMosh,

I enrolled through Coursera platform to get the certificates. And in there DeepLearning.AI and Stanford co-exist including what the certificates write on them.

That is only for Machine Learning Specialization. The rest courses offered by DLAI are solely the product of DLAI.


Hi @Menelaos_Gkikas for me what work the most is to picking up the fundamentals on python and started to build projects and simple programs. I love datacamp, it has amazing courses but for me the feeling of watching a lecture and translate into an editor and have the feeling of managing environments, downloading libraries and having to figure out myself whats wrong help me to build foundation on what should I expect from real problems.

Plus ChatGPT can be an amazing learning tool that will help you to learn and explore new things faster.

I am not sure about your own conditions, but if I had to give an advice for myself with the current technology I will suggest to myself pick a course with the fundamentals on python with a lot of theory and I will use ChatGPT to explain me and assist with the practical part.

I hope this helps

Hello pastorsoto,

It’s true that technology, cloud and already-made infrastructure offer treasures when it comes to programming.

Personally speaking I’m oriented in terms of building a great CV as well as following my creative passions. If we’re enrolled in 3 different world-class educational platforms that monitor progress and performance on a weekly basis, it would be impossible each time someone proposes a platform or an idea based on their experience, follow and align with what he said especially if what’s needed is strategic thinking.

That would be impossible. Of course, programming & technology take time and I’m not willing to compromise in terms of being left behind of technological breakthroughs. But we need some sense of multicriterial optimization.

Thanks a lot for the advice…!