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Explore Topic Modeling using scikit-learn and Non Negative Matrix Factorization (NMF) with 9,970 views, 157 likes, and 9 comments. Experience the impact of this video content that has captured audience attention.

#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)

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