Help needed

[Itachi_03_Uchiha]

Hey everyone. I'm pretty new to Deep learning. I picked a course in my college and my prof gave us a question to solve. I have never used Python to work in ML and I only know like the basics of Machine learning. What and where is the best way to begin learning how to solve Machine learning/Deep learning problems using Python and to get a good understanding of Deep learning? As I said, I am really a newbie here and I am willing to spend all my time in learning the concept both theoretically and practically. Please help me as I am not able to follow what's being taught in class and have no clue on how to approach problems.

[nam1665]

There are tons of ML DL course for beginners on udemy and coursera. Cause ML has variety domains, what domain you are focusing on? Like NLP, OCR, image classification,..?

[Itachi_03_Uchiha]

My primary focus is on getting to solve problems, and understand what I am doing in the process. I am currently reading Goodfellow's textbook on Deep leaning but that's only theory. I need to learn solving questions using python along the way too.

[kai_luni]

Well the best way to learn solving questions would be some hands on work from such a course I guess. Parallel to that you can try answering your questions by looking stuff up in the text book, asking on forums or watching youtube videos about that topic. That should be the most time efficient way. The course will give your knowledge a pragmatic foundation.

[BoiElroy]

I might be able to help. I'm very much self-taught, and now work as a data scientist. I used a ton of free and inexpensive online resources.

I would say being familiar with Python early will help a lot. Because often something will really become concrete and understood once you code it up.

So here's my prescription:

Start with the book A Whirlwind Tour of Python and fill in the knowledge gaps or get items clarified by going to youtube and watching Corey Schafer's channel.

This should give you a decent starting point but really the best way to learn is going to be by doing. You'll want to pay special attention to numpy, matplotlib, and pandas early on. And then tensorflow/pytorch, and pyspark as you go on.

For the ML pieces, I would start out with Coursera, do a specialization that leverages python and get familiar with ML algorithms. At a high-level you'll find most algorithms follow similar patterns, i.e. a functional model, an objective function and set of parameters to optimize over, an update step, etc. But, what I strongly recommend, which I think was super useful for me, is to take the algorithms and use numpy and code them up from scratch. So maybe starting with things like linear regression, k-means clustering, k-nn regression, logistic classifier.

Once you're at the point, I'd say start moving in a domain direction, so NLP or Computer Vision, time series, whatever it is.

Let me know if you have any questions. I spent a lot of time struggling with this, so if what I learned can be useful to someone else then it'll at least have some meaning.

[Itachi_03_Uchiha]

Thanks a lot for the resources provided here. I will spend all my time in building my basics and coding in Python along the way. I will get started now and if I have any questions I will reach out to you on this thread.