Please explain solution

Given the training data below, execute the following 3 steps, the first two create the HMM and the third uses this HMM for decoding: (a) calculate the emission probabilities for each word given each POS; (b) calculate the transition probabilities; (c) draw a chart where the columns are positions in the sentence and the rows are names of states (Start, POS tags) and fill in the probability scores assigned by the Viterbi algorithm assigning POS tags to the test string "flying planes". Assume P(Start) 1.0 Training Data: buffalo/NNS flying/VBG is/VBZ dangerous/j flying/J planes/NNS are/VBZ numerous/ /PRP saw/VBZ Mary/NNP flying/WBG planes/NNS He/PRP planes/VBZ shelves/NNS Test String flying planes