Hello to everyone,
I am looking to complete a Masters degree in AI as a self-study, and today I signed to Machine Learning Specialization today. However, can anyone share a list of courses/books that would cover a full Masters self-study, either on Coursera or in-general?
Though it is a few years out now (i.e. there is nothing about transformers or LLMs) ‘Deep Learning’ from Ian Goodfellow, et al. is considered a classic in the field.
However, note, even though they cover the required Maths in the first few chapters of the book, this text remains highly technical, so you’d need to feel comfortable with that / get to that point.
Thank you. To better understand the scope, is it reasonable to expect that I study the whole book, cover to cover, in 1 semester? Or would this take 2 semesters? I am trying to create a realistic plan of study.
@borisreitman In the end a lot will depend on the present level of your math and programming skills.
For example this University of Colorado, Boulder course covers the book over one semester, but does not address all topics/chapters.
https://home.cs.colorado.edu/~DrG/Courses/NeuralNetworksAndDeepLearning/CourseContent.html
Personally I would finish the machine learning specialization and deep learning specializations first. For the latter the Goodfellow text can be used as a secondary reference for more details.
For ML, I may be slightly biased-- I took my ML Cert via HarvardX under Prof Irizarry, whom (like Prof Ng) is an excellent teacher. This was the ML text we used:
Only thing is that class is taught in R, not Python, so it won’t directly help you with courses here. Of course also important is to realize it is not as if Python is the only language used for these tasks. There is R, Julia, SASS, etc.
Hope that helps a bit.
Thank you.