I learn about Uncertainty Reasoning in Machine Learning. There are three types of machine learning, namely Supervised Learning, Unsupervised Learning, and Reinforcement Learning. Supervised Learning allows collection or production of data from previous learned experienced to be added to the machine. Unsupervised Learning on the other hand happen in real time without any addition of additional data; So all input data analyzed and labeled in the presence of learners. Yet, it is easier to get unlabeled data than labeled data, which needs manual intervention. However, as not all model can be used for all possibility of situation, thus a probability representation should be used so that the best model can be created for a certain condition. Through this, Axiom of Probability theory is created. In short, exist a rule namely Bayes rule is used in this theory. Through the rule, exist Naive Bayes Classifier, which is a classification techniques based on Bayes theorem with an assumption of independence among predictors.