Machine Learning (ML) has become state of the art for various tasks, including classification of accelerometer data. In the world of Internet of Things (IoT), the available hardware with low-power consumption is often microcontrollers. However, one of the challenges for embedding machine learning on microcontrollers is that the available memory space is very limited, and this memory is also occupied by the rest of the software elements needed in the IoT device. The problem is then to design ML architectures that have a very low memory footprint, while maintaining a low error rate. In this paper, a methodology is proposed towards the deployment of efficient machine learning on microcontrollers. Then, such methodology is used to investigate the effect of using compression techniques mainly pruning, quantization, and coding on the memory budget. Indeed, we know that these techniques reduce the model size, but not how these techniques interoperate to reach the best accuracy to memory trade-off. A Convolutional Neural Network (CNN) and a Human Activity Recognition (HAR) application has been adopted for the validation of the study .