Model counting, or counting solutions of a set of constraints, is a fundamental problem in Computer Science with diverse applications. Since exact counting is computationally hard (#P complete), approximate counting techniques have received much attention over the past few decades. In this chapter, we focus on counting models of propositional formulas, and discuss in detail universal-hashing based approximate counting, which has emerged as the predominant paradigm for state-of-the-art approximate model counters. These counters are randomized algorithms that exploit properties of universal hash functions to provide rigorous approximation guarantees, while piggybacking on impressive advances in propositional satisfiability solving to scale up to problem instances with a million variables. We elaborate on various choices in designing such approximate counters and the implications of these choices. We also discuss variants of approximate model counting, such as DNF counting and weighted counting.