100 Days of Machine Learning Code Day 12

Day 12 Exploring NLP , We all are familiar with Siri, Cortana , Google Asistant  , Alexa  but being a beginner scratching the surface and starting with NLP is way slower in academic curiculum with majority of part being understanding parts of speech  , speech tagging, finding out and correcting sentence structure with even find finding words with similar word ending (memorising past participle of words in masters seems way over the top) the algorithms on the other hand are intersting such has - "Levenshtein distance"  which is used in things like autocorrect and word prediction in smartphone keyboards  also learnt about how the POS tagging leads to intent classification  (intent classification is major step in making smart assistant skills in Alexa or Google Assistant ) so searched around on more on Implementation updating keyword dictionary on terms like BERT  , new Nvidia Jarvis for recent Nvidia GTC 2020 , from both programming standpoint and NLP ML learning , the minimum edit distance question does make it experience so made a python program in colab for calculating the Minimum edit distance 

Here is my Notebook for calculating minimum edit distance