Topic Modeling using scikit-learn and Non Negative Matrix Factorization (NMF)

#datascience #machinelearning #ml

Link to Topic Modeling using LDA video - https://www.youtube.com/watch?v=25JOEnrz40c
Link to playlist containing banking ML use cases - https://www.youtube.com/playlist?list=PL3N9eeOlCrP4uLCtas5vxq09sWz6jJXrw

In this video we will see how to build topic model using non negative matrix factorization

NMF stands for non-negative matrix factorization, a technique for obtaining low rank representation of matrices with non-negative or positive elements

we factorize a matrix X into two matrices W and H so that X = WH

The matrix W is generally called the dictionary or basis matrix, and H is known as expansion or coefficient matrix. The underlying idea is that a given data matrix A can be expressed in terms of summation of k basis vectors (columns of W) multiplied by the corresponding coefficients (columns of H)

Видео Topic Modeling using scikit-learn and Non Negative Matrix Factorization (NMF) канала AIEngineering