An advice to a self-learner of computational complexity

I'm a mathematics undergraduate student (going to start my third year very soon). I'm trying to teach myself computational complexity. Sadly, there are no courses provided in my university on the topic and there are no specialists on it (In fact, it seems that my university has no specialists in theoretical CS at all). So, I have no choice to learn the topic except via self-study. I've already gone through the first few chapters of Computational complexity: A modern approach [ but I did not solve any of the exercises (except two or three in the first chapter exposing what are TM's and Halting problem) ]

I face some problems with the exercises of in the text:

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one thing is that I feel that the kind of thinking needed in computational complexity is quite different than that of mathematics that I'm familiar with (There is an emphasize on algorithmic thinking etc and how to reduce problems to other problems and to make a TM simulating another TM etc) which is something I find difficult.

Is there any way do you recommend to help me developing this level of maturity about algorithms, reductions etc?

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One reason of difficulty of "1" is that I don't know to what extent should I be rigorous? For example, when I'm doing a reduction or a simulation, it's possible that I can see intuitively that something is clear and can be done but the details would be very very tedious to actually carry out and hence I feel that I don't understand the thing very well. The point is that, I know that there must be a compromise between rigor and intuitive thinking. But since I've no guide nor an instructor, I don't know where this line should be.

when should I carry out all the details if something somehow clear and when should I be satisfied with the intuition? It seems that there are much more details in computational complexity than is usually present in the math I know and hence, where the compromise should be?

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I wonder if there exists any source of exercises\problems that

My recommendation is to focus on two things: prerequisites, and practice.

Prerequisites: It's going to be harder to learn computational complexity without first having a good understanding of algorithms. At many schools, a class in algorithms is a prerequisite to the class on computational complexity. You mention you feel you may lack maturity in algorithms, and in your position, who wouldn't? It's a non-trivial subject that you haven't studied. So, my first piece of advice is: before spending too much time on computational complexity, first spend some time studying algorithms. There are many good algorithms textbooks and online courses (e.g., via Udacity, Coursera, or EdX). It might be helpful to spend some time learning that material. You don't need to learn every algorithm in the world, but try to get a feel for some common algorithmic techniques; designing reductions is basically designing an algorithm, so this knowledge will be helpful. Also, make especially sure you are comfortable with proofs of correctness for algorithms and asymptotic running time analysis.

Next, practice. No one starts out with a maturity with reductions. The way to get more comfortable with that is to practice -- do exercises until you either feel comfortable with the material, or the exercises uncover some gap in your understanding (which you can then go back and focus on learning more about). You probably can't learn the material without this kind of practice.

To help you practice, it may be helpful to look for an online course (a MOOC) on computational complexity. You can check Udacity, Coursera, or EdX.

How rigorous should you be? Rigorous enough that you are convinced that you could fill in all the details if you needed to (and that a fellow classmate at a similar level of studies could fill in all the details without thinking hard -- they'd agree filling in the details is tedious but straightforward and obvious).

StackExchange Computer Science Q#61170, answer score: 7