I have basically no coding background. What is the fastest and easiest way to learn enough Python to understand this course?
There’s no magic recipe. Code, code and code.
My advice is: go to Hackerrank and similar sites and start there, go to YouTube and search for: “Python projects for beginners”, and first of all try to do it by yourself.
I hope this helps you a little. If not, feel free to contact me and I will try to help you as much as I can.
Regards,
Lucas
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@lucaswener is right: there is no magic recipe.
You said that you have “basically no coding background”. That makes it tough: many Python introduction are for people with a previous programming background in other languages.
There are lots of introductions to programming available: they have varied expectations about what you already know. I’d suggest that you look at a few to see which ones seem to suit your taste. Really important: write code every day; make mistakes, learn to debug them.
While learning some programming and Python, I’d also suggest watching the video lectures for this course without worrying about programming detail: then watch them again when you feel ready to really tackle the material. Just watching the videos can be a good “manager’s introduction” to the neural networks topic.
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To add to what Lucas said, if you have no coding background it might be useful as well to take a short evening course (preferably in person) like “data science in python” or “python for data science”. This will teach you the specific python elements for DL, instead of the more generic Python functions. Not sure where you are located, but there often are evening classes or short introductions like that in the bigger cities. I did that myself as well (as I was familiar with a lot of programming languages like Pascal, C, etc but not with Python) and it really speeded things up.
Alternatively, there are some short and inexpensive online courses on the different well-known platforms if you search on the same topic (python for data science), but I do not know the quality and whether these are hands-on.
Such a type of course will basically give you a head start. After that, it’s indeed code, code, code. Hackerrank is a fun and great way to get to grips with that.
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Hi @Pacific64,
There is a lot of good advice in this thread.
Kaggle has a nice short introduction to Python. If you feel it’s a bit complicated, in the first lesson there’s a link with resources for first-time coders.
There’s also a Python specialization on Coursera that you could take later on.
I really hope you enjoy it. Coding is beautiful 
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Hi ! I would suggest to simply bite (byte 
) the bullet and follow the python tutorial. It’s really good: The Python Tutorial — Python 3.9.4 documentation, until section 8 or maybe 9.
This is probably one of the best sources if you’re serious about python.
If you just want to know enough for data science, I’ve written a short python crash course, maybe it can be useful : Python Crash Course for Machine Learning
mentors : I’m not sure this is ok to post this last link… my goal is not to do self promotion here, just to help. Please let me know and I will delete it if needed.
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I recommend Corey Shaffer on youtube for lectures.
Its free and better than most Udemy courses.
In the end, you will need practice. So set up a problem
for yourself. e.g. try implementing a popular numerical
algorithm with matrix multiplication and for/while loops and implement
it yourself.
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One bit of advice I can share reflecting on my own journey into Python and Deep Learning is to take the time to study and understand numpy. Getting a handle on applying vectorization for linear algebra operations is very important and I think its something most traditional Python courses don’t cover extensively. And like others said - practice is key. Frustration is normal in this process.
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do you suggest any resources for studying numpy?
I recommend the book “Python for Data Analysis” by Wes McKinney
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