(Part 2) Best products from r/learnmachinelearning

the 'theoretical' roadmap I think is arguably the easier one to organize. There might be faster ways to get a rough idea, but if you want the BS foundation, you'll basically want to work up to Bishop's pattern recognition and machine learning, and elements of statistical learning. Applied predictive modeling is a great practical book, that one alone could get you enough to be dangerous on Kaggle, but the deep understanding will come as you push up into the other two.

Bishop's is the easier of the two, but it'll still take a pretty mature grasp of statistics, proof based mathematics, multivariable calculus, and even a little bit of calculus of variations. How's your linear algebra? I've just started poking into Boyd's intro book just for shits and giggles (sometimes it's fun to go back and play the early levels in a game you've mastered, haha) and I'm liking the organization and topics quite a bit, I think it'd be a solid one to learn from the first time. Strang's Linear algebra book would also be a really good choice... you'll learn a little more from Strang's, but see far fewer meaningful applications that are relevant to what you're wanting to head towards. I've heard Strang's is good for calculus too if you still need that as well.

I like Wasserman's 'All of Statistics', but that book's no joke. If you aren't comfortable with proof based mathematics yet, you'll need to start there. Alcock's 'how to think about analysis' is an incredible primer, you can blow through it in a week and you'll learn a ton (if you haven't already taken a course in real analysis). If you need a primer on proof based math, this one looks like a good pick but I haven't gone through it myself yet, so I'm a little less up on that one.

Oh, you don't need combinatorics for stats (you can muddle your way through without it) but it will come up. If you really want to make sure you have rock solid fundamentals, 'a walkthrough combinatorics' is a really incredible book. It's a strange one, the chapters are fairly short and easy, and then there's always like 20 really bizarre problems. Not like 'apply what you've learned in a mechanical way' but like, REAL math puzzles. It'll be a discouraging book if you just want to blow through, but if you want something to push you to think crazy things you've never thought before, that's a great place to pick up some combinatorics and practice your proof skills.

I've been through a good chunk of that list so far over the last two years, and while there's still an absolutely hilarious amount to learn, I'm starting to see things as they are, it's pretty cool. A lot of ML architectural choices now feel very well motivated (what's softmax? Where does the cross entropy loss function come from? Why might the KL divergence be a good choice for unsupervised classification? Why is the Wasserstein metric an improvement for GAN stability?) but... Christ there's a lot to learn, haha. So if you're going to do tackle the theory, buckle up for the long haul, and make sure you're ALSO consistently making headway on the applied side. Code every day, always have something cool you're excited to be building. Work through fast.ai, or implement your own ML library. Hell, use CUDA if you like, or do a project to scrape your own data or... you know. There's a million things to do, make sure you don't wait until you're done with your math before you start doing cool stuff. Getting in over your head and doing stuff that you don't fully understand can be a great way to learn too. Plenty of times I've had math stuff 'click' because it grounds something I did a year or two back and didn't 'get' fully at the time. t-SNE and so on...

Anyway. Good luck. It's a long journey, but in my experience so far at least, if you know your shit there's opportunity out there for work too. But you'll need to really dig in to differentiate yourself, and you'll need to get good at networking. Getting a first job without a degree will put you at a disadvantage, but it's a disadvantage that'll disappear by your first two or three years working in the industry it seems like. Experience talks.

First 6 weeks of Andrew Ng's [basic ML course] (https://www.coursera.org/learn/machine-learning), while reading Intro to Statistical Learning, for starters (no need to implement exercises in R, but it is a phenomenal book).

From there you have choices (like taking the next 6 weeks of Ng's basic ML), but for Deep Learning Andrew Ng's [specialization] (https://www.coursera.org/specializations/deep-learning) is a great next step (to learn CNNs and RNNs). (First 3 out of 5 courses will repeat some stuff from basic ML course, you can just skip thru them).
To get into the math and research get the Deep Learning book.

For Reinforcement Learning (I recommend learning some DL first), go through this [lecture series] by David Silver (https://www.youtube.com/watch?v=2pWv7GOvuf0) for starters. The course draws heavily from this book by Sutton and Barto.

At any point you can try to read papers that interest you.

I recommend the shallower, (relatively) easier online courses and ISLR because even if you are very good at math, IMO you should quickly learn about various topics in ML, DL, RL, so you can hone in on the subfields you want to focus on first. Feel free to go through the courses as quickly as you want.

I think it might be too early for you to actually study machine learning. Math prerequisites are extremely important and 'math in school' won't cut it. From your perspective it's a very narrow subdomain of knowledge, which can be mastered only after you go through the math, I'm afraid. I don't mean to discourage you. I don't know how much time you have to study every week, but unless you treat pursuing things that interest you as a full-time job, then it will be very hard to learn web development, machine learning and whatever comes in between.

That being said, I have some recommendations. First one is a book. It's a nice high level introduction to machine learning techniques. You should be able to follow it easily even without solid maths foundation. The other recommendation is a course: AI for Everyone. Andrew Ng is one of the machine learning most known popularizers. His other course, Machine Learning, is a starting point for many people trying to find out what machine learning is, but it expects some math intuition from you.

Good luck!

Lose the swift programming course, it’s not really relevant to you, and you already have a lot to cover in a tight space of time.

Good luck with your studies. As others have already said in this thread getting a researcher position will be super hard. There aren’t all that many positions available, and there’s so much hype around ML that they’re all super over-subscribed. You might be right that you don’t need a PhD, but a PhD and research experience are useful and you will be up against those that have them. You should consider getting some industry experience as a data scientist or data engineer (which might be a bit easier to get hired as) to complement your self study if you’ve decided academia is not for you.

You’ve got a lot of reading to do already, but I found the book So Good They Can’t Ignore You a helpful read when faced with a tough career choice. It’s not super long, and has some interesting ideas (mostly based on anecdotal evidence but useful nonetheless).

/u/LengthContracted this is a good book, as is Daphne Kollers book on PGMs as well as the associated course http://pgm.stanford.edu

A sample of what is on my reference shelf includes:

Real and Complex Analysis by Rudin

Functional Analysis by Rudin

A Book of Abstract Algebra by Pinter

General Topology by Willard

Machine Learning: A Probabilistic Perspective by Murphy

Bayesian Data Analysis Gelman

Probabilistic Graphical Models by Koller

Convex Optimization by Boyd

Combinatorial Optimization by Papadimitriou

An Introduction to Statistical Learning by James, Hastie, et al.

The Elements of Statistical Learning by Hastie, et al.

Statistical Decision Theory by Liese, et al.

Statistical Decision Theory and Bayesian Analysis by Berger

I will avoid listing off the entirety of my shelf, much of it is applications and algorithms for fast computation rather than theory anyway. Most of those books, though, are fairly well known and should provide a good background and reference for a good deal of the mathematics you should come across. Having a solid understanding of the measure theoretic underpinnings of probability and statistics will do you a great deal--as will a solid facility with linear algebra and matrix / tensor calculus. Oh, right, a book on that isn't a bad idea either... This one is short and extends from your vector classes

Tensor Calculus by Synge

Anyway, hope that helps.

Yet another lonely data scientist,

Tim.

You can check out sites like HackerRank. It has problems from the book,

CrackingTheCodingInterview

I like and recommend this book as well,

Introduction to Algorithms

Great book to prepare for Machine Learning Job Interviews:

https://www.amazon.com/Cracking-Machine-Learning-Interview/dp/B07K4Y6T3J

These both seem like okay books. I would actually recommend reading CLRS introduction to algorithms Introduction to Algorithms, 3rd Edition (The MIT Press) https://www.amazon.com/dp/0262033844/ref=cm_sw_r_cp_api_i_zme3DbCFYP00F
This teaches you algorithms that are less specific to a particular language. But if you really want to focus on python I think the books you picked are okay but won’t Supplement the math background.