I'm trying to understand algorithm complexity, and a lot of algorithms are classified as polynomial. I couldn't find an exact definition anywhere. I assume it is the complexity that is not exponential. 

Do linear/constant/quadratic complexities count as polynomial? An answer in simple English will be appreciated :)

First, consider a Turing machine as a model (you can use other models too as long as they are Turing equivalent) of the algorithm at hand. When you provide an input of size $n$, then you can think of the computation as a sequence of the machine's configuration after each step, i.e., $c_0, c_1, \ldots$ . Hopefully, the computation is finite, so there is some $t$ such $c_0, c_1, \ldots, c_t$. Then $t$ is the running time of the given algorithm for an input of size $n$.
An algorithm is polynomial (has polynomial running time) if for some $k,C>0$, its running time on inputs of size $n$ is at most $Cn^k$. Equivalently, an algorithm is polynomial if for some $k>0$, its running time on inputs of size $n$ is $O(n^k)$. This includes linear, quadratic, cubic and more. On the other hand, algorithms with exponential running times are not polynomial.
There are things in between - for example, the best known algorithm for factoring runs in time $O(\exp(Cn^{1/3} \log^{2/3} n))$ for some constant $C > 0$; such a running time is known as sub-exponential. Other algorithms could run in time $O(\exp(A\log^C n))$ for some $A > 0$ and $C > 1$, and these are known as quasi-polynomial. Such an algorithm has very recently been claimed for discrete log over small characteristics.
