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Resource-efficient ML for edge and endpoint IoT devices is a field of active research and increasing development. Libraries have been providing support for machine learning enthusiasts to run ML algorithms in the cloud. Executing ML algorithms on motes is a challenge, as resources are highly constrained. To optimally use these resources, the developer often needs to have complete knowledge of the underlying architecture. VerticalThings is a domain-specific language (DSL) developed for programming ML-based embedded applications. The language offers constructs for key platform functions such as resource management, concurrency, task isolation, and security. This enables static analysis of (a) important safety and security properties, and (b) timing and power considerations. To enhance this DSL further, we developed a DSL named FieryIce which provides intelligent learning of parameters based on sensor data. We would integrate both the DSLs within the IDE developed for VerticalThings. The learnable parameters are learnt at compile time avoiding the use of scarce memory of the embedded systems. This paper shows the capabilities of a domain-specific language (DSL) named FieryIce which is designed to help embedded developers use VerticalThings and develop ML-based embedded applications with ease. The contributions of this article are: (a) A domain-specific language (DSL) named FieryIce and it is capabilities to perform machine learning-related tasks; (b) how FieryIce helps students better their understanding of machine learning algorithms.