Build your own Movie Recommendation Engine using Word Embedding

You may have noticed that services like Netflix, Amazon Prime, YouTube recommends the same kind of video you have watched earlier. So, how does this happen, it is because of Machine Learning based recommendation engine which determines how similar the videos are to other things you like and based on that assumption it serves upContinue reading “Build your own Movie Recommendation Engine using Word Embedding”

Natural Language Processing with N-Gram Model

Till now we have seen two natural language processing models, Bag of Words and TF-IDF. What we are going to discuss now is totally different from both of them. Also, the applications of N-Gram model are different from that of these previously discussed models. To understand N-gram, it is necessary to know the concept ofContinue reading “Natural Language Processing with N-Gram Model”

Term Frequency-Inverse Document Frequency (Tf-idf)

Term Frequency-Inverse Document Frequency is a natural language processing model use for converting textual data into numerical form. Principle is, as number of times a word increases in given data, the Tf-idf value increases correspondingly. Or, importance of a word is proportional to the number of times it appears in the data but is offsetContinue reading “Term Frequency-Inverse Document Frequency (Tf-idf)”

What is Bag of Words and How to Code it in Python

We all know that Machines can only read numbers and mathematical calculations and cannot read textual data like us. Bag of word helps to convert textual data into mathematical form that can be applied to any machine learning algorithm. Creating Bag of Words Let’s make the bag-of-words model concrete with the following example. paragraph =Continue reading “What is Bag of Words and How to Code it in Python”