The Hierarchical Temporal Memory Cortical Learning Algorithm (HTM CLA) is a theory and machine learning technology that aims to capture cortical algorithm of the neocortex. HTM consists of 2 different components: Spatial Pooler and Temporal Memory. Inside the algorithms, there are multiple mini columns act as synapses in our brain. These columns will be activated or deactivated depend on the input that is given. This is similar to the synapse activity. HTM, like many other machine learning algorithm, only deals with number. Therefore, it requires an encoder to transform the real world concept into digitized world of '0's and '1's.