This post aims to explain the concept of Word2vec and the mathematics behind the concept in an intuitive way while implementing Word2vec embedding using Gensim in Python. The basic idea of Word2vec is that instead of representing words as one-hot encoding (countvectorizer / tfidfvectorizer) in high dimensional space, we represent words in dense low dimensional space in a way that similar words get similar word vectors, so they are mapped to nearby points. Word2vec is not deep neural network, it turns text into a numeric form that deep neural network can process as input. For example, we can use "artificial" to predict "intelligence". However, the prediction itself is not our goal.