You need a way to organise and prioritise these. Use a Trello board.
Because you're self-studying, you're sort of going to a self-study university taking different modules from different faculties. This Trello board spells out your self-study university curriculum and you're in charge of it.
Here is a 5-step process to build this curriculum.
Firstly, create say 3 lists on the Trello board: ML, CS and Math. Each list represents a 'faculty'.
Then, for every list, create Trello cards where each card is a 'module'. For example, you would create 'Data structures & algorithms' in the CS list and 'Decision trees' under ML.
The next step is to figure out for each module if it's something you either (i) wish to know or (ii) must know. You can use Trello labels or even use the Trello separators for this.
This following step requires a bit of work and it's the fun part, only because it's self-study. For each module, list down (you can list things in a card) the resources you have for that module. For this there are various resources you can get from the comments, search engines, and your peers. Consider the different modes of instruction: books, e-books, videos, lecture notes, slides, articles, blog posts, online learning platforms and so on. Choose what's best for you. If you can't decide just pick something first and find another time to source for another material.
Lastly, prioritise the modules. This can be done by easily dragging the modules which you want to do first on top of the list (having considered what you wish to know and what you must know). Set, say, top 3 modules for each list then you'd like to do for the next 2 weeks.
This is the high-level curriculum planning. If you plan on a micro-level planning like what modules to do for this week or for today, that I leave it to you.
There's so much resources it's hard to pick… If you're already studying CS and like to learn by building, I'd recommend to do that for ML too!
Pick a real problem, try to build a ML solution for it and while doing so keep a list of things you'd like to dig deeper into. Then go back to that list and pick one item to study, and iterate.
Happy to have a chat and give you specific pointers if you'd like (email in profile), I got my master in ML in 2016 and applied it in the industry since.
I self study machine learning here https://learnaifromscratch.github.io/ai.html it's an early and shitty draft and proof of concept that you can do self-directed learning for these topics while looking up the background you need to know, which for me is much more interesting than taking a generalized math curriculum of absolutely everything. The courses so far we haven't escaped the content of Wasserman's 'All of Statistics' book yet on classification or probabilistic graphs, so you could if you wanted watch the lectures and only do Wasserman's book.
If you want to try the OCW linear route of taking everything for whatever reasons, you will have to get up to MIT student levels trying to unravel the algebra done in the early calculus courses and later where they just assume you possess this background. One way to do that is those problem solving books like this one which 'bridges the gap between highschool math and university' https://bookstore.ams.org/mcl-25 at least you then get worked out solutions. Another way is Poh-Shen Loh's Discrete Math course he opened up on YouTube which is done the same way he holds the CMU Putnam seminar, working through a bunch of combinatorics and algebra will more than prepare you to understand those continuous math OCW courses https://youtu.be/0K540qqyJJU
Like everybody else there is of course the issue of: who is going to check my work. For me I went with the time tested tradition of hiring a tutor, a local grad student and paid them once a week to go on chat/zoom or meet at a coffee shop before the pandemic and spend a few minutes going over everything I'm doing wrong. In the early days however I used constructive logic ie: 'proof theory', to audit my own work: https://symbolaris.com/course/constlog-schedule.html and read a huge amount of Per-Martin Lof papers on the justifications of logical operators like implies, disjunction, conjunction, etc. Of all the math I've ever taken I would say that proof theory was the most useful for somebody by themselves who isn't sure of what they are doing (I'm still not 100% sure.. hence why I hire people now).
If you want a great Calculus text that explains those nasty looking Euler's e nested statistics distributions try Mathematical Modeling and Applied Calculus by Joel Kilty everything from partial derivatives, gradients, x^n, e^x, trig, integrals, limits is explained in terms of parameters to modeling functions, if you write software it will be easy to understand. I haven't posted it yet but I tried going through Allan Gut's probability book using only that math modeling calc text and have not run into anything applied, as in concepts about limits or integrals, that wasn't already covered. Of course the concepts are much more abstract measuring a bunch of intervals and a different method of integration and I don't pretend I'll be making any advances in this area beyond applied usage but it can be done, jump in and pick up the background as you go as opposed to doing all the background at once, losing interest and giving up.
I'm coming to the end of my first year (6 year part time) Comp Sci course and have seen that we have options for AI and Machine Learning modules in future years. Where should I go to find something like a list of what I should be brushing up on, or learning completely from scratch, in order to not fall flat on my face during those type of modules.
I understand there are very set starting points in math subjects because concepts build on one another but I don't know what I should be starting with and where to go afterwards.
Perhaps consider self studying the big ideas? We have a product manager who is self-studying to boost his ML street cred on the project he is managing currently. You only need a handful of books/courses to get 80%+ of the way there. The math isn't all that bad if you're mathematically inclined, and even if you're not, you just need to know symbolically what is going on. The implementations are already done (via sklearn, etc.)
these are things I might consider if I was in your situation:
1) build something that you think will force you to learn whatever data structures and algorithms you want to learn. Two benefits; learning and having something to show for it
2) ask someone to do mock interviews with you to practice
3) take the coursera ML course - fascinating, useful, marketable skill and hard to do unless you have a couple of months free to dedicate
4) network. Coffee with people IRL regularly
This seems wildly out of reach even for people who make good money as SWE's.
I agree, being self taught is kind of a misnomer. I've hired PHD students to teach me maths concepts I forgot that I know I'd have no idea how to teach myself. PHD + open courseware or online stanford courses has done wonders since I'm getting properly back into ML / AI stuff.
Another comment mentions teachyourselfcs.com. I can vouch for that. I am a chemical engineering undergrad who taught himself computer science mostly using those resources.I had fair bit of interest in becoming a machine learning researcher and I had good enough experience in programming, so wanted to learn CS in depth.
Incidentally, I started a company called Primerlabs(https://Primerlabs.io) which creates self paced conversational CS courses to learn on your own. Although there are only two courses now.
So, I will say go with teach yourselfcs. Other listicles are too much info, Imho.
Hi, sorry I missed this message. You probably won't see this, but my plan is to do the following (you can Google the course names):
1. Learn math (I majored in history and sociology)
Classes
-------------
Linear Algebra: Foundation to Frontiers (UT Austin)
Linear Algebra, MIT OpenCourseware
Single-Variable Calculus, MIT OpenCourseware
Multi-Variable Calculus, MIT OpenCourseware
Probability Theory, Stanford CS109
2. Learn machine learning
Machine Learning, Stanford CS229 (taught by Andrew Ng)
Classes @ deeplearning.ai (also taught by Andrew Ng)
You can do both. If you are keen on understanding the concepts, there are many self-learning courses available online (both for math/linear algebra concepts etc., and data science/analytics).
I'm trying to get more comfortable with ML (I have an okay theoretical understanding of quite a few things, but very little experience writing actual code).
My plan is to expose myself to new concepts or go more in depth on things I already know about (planning to go through a few books I have been meaning to, for example), and then whenever my memory fails me about something I already understood, go back to the best resources on that subject that I have saved from the first time I explored it.
This sort of spaced repetition works wonders for me.
I'll then complete a couple of courses to familiarize myself with syntax of at least one commonly used framework (thinking fastai which uses pytorch, give me suggestions if you got a better one).
I've also started reading the SRE book from Google after developing an appreciation for my coworkers who do that sort of thing.
I also want to study music (guitar specifically) but I don't know if I can find a good resource that includes material to study on a weekly basis for example or if I will have to get a tutor.
Just look up a class you want to take, figure out what the prerequisites are (often listed in the course description or syllabus), and follow the chain of prerequisites until you hit a level that's appropriate.
You can easily look do this with a few different universities and just compare their courses for a particular subject, find the most common textbooks, etc.
And nowadays, this is even easier since you can find a MOOC or online course for basically any course in the DAG leading to nontrivial machine learning.
(Programming languages should never be a factor, unless you're choosing between two otherwise equivalent offerings.)
I want to learn machine learning but I don't want to just be a practitioner, I want to understand how all the nuts and bolts work. How do I self study this?
First month covers the basics (calculus, algebra, probability, algorithms)
Second month is focused around coding (Data science in Python) and putting the things learned in the first month in a ML context (Siraj's Math of Intelligence course on YouTube)
Because you're self-studying, you're sort of going to a self-study university taking different modules from different faculties. This Trello board spells out your self-study university curriculum and you're in charge of it.
Here is a 5-step process to build this curriculum.
Firstly, create say 3 lists on the Trello board: ML, CS and Math. Each list represents a 'faculty'.
Then, for every list, create Trello cards where each card is a 'module'. For example, you would create 'Data structures & algorithms' in the CS list and 'Decision trees' under ML.
The next step is to figure out for each module if it's something you either (i) wish to know or (ii) must know. You can use Trello labels or even use the Trello separators for this.
This following step requires a bit of work and it's the fun part, only because it's self-study. For each module, list down (you can list things in a card) the resources you have for that module. For this there are various resources you can get from the comments, search engines, and your peers. Consider the different modes of instruction: books, e-books, videos, lecture notes, slides, articles, blog posts, online learning platforms and so on. Choose what's best for you. If you can't decide just pick something first and find another time to source for another material.
Lastly, prioritise the modules. This can be done by easily dragging the modules which you want to do first on top of the list (having considered what you wish to know and what you must know). Set, say, top 3 modules for each list then you'd like to do for the next 2 weeks.
This is the high-level curriculum planning. If you plan on a micro-level planning like what modules to do for this week or for today, that I leave it to you.
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