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Cardinality estimation is a fundamental task in database query processing and optimization. As shown in recent papers, machine learning (ML)-based approaches can deliver more accurate cardinality estimations than traditional approaches. However, a lot of example queries have to be executed during the model training phase to learn a data-dependent ML model leading to a very time-consuming training phase. Many of those example queries use the same base data, have the same query structure, and only differ in their predicates. Thus, index structures appear to be an ideal optimization technique at first glance. However, their benefit is limited. To speed up this model training phase, our core idea is to determine a predicate-independent pre-aggregation of the base data and to execute the example queries over this pre-aggregated data. Based on this idea, we present a specific aggregate-enabled training phase for ML-based cardinality estimation approaches in this paper. As we are going to show with different workloads in our evaluation, we are able to achieve an average speedup of 63 with our aggregate-enabled training phase.