Free Machine Learning Tutorial – Sparse Matrix
Learn about sparse matrices, their representations, operations, and applications in this comprehensive course. Improve computational efficiency and storage space with Java coding examples. Perfect for undergraduate, postgraduate, and research scholars working with large datasets.
This course discusses about sparse matrices, its representations & operations and applications. Sparse matrix is one of the linear data structures to deal with non zero elements. If a matrix consists of more number of zero elements, storing those zeros and processing them would decrease computational efficiency. Use of sparse Matrix improves computational efficiency in terms of storage space and computational time. This course would be more useful to undergraduate, postgraduate students and research scholars who work with large data sets embedded with more zero elements.
This course teaches, the ways to represent sparse matrix such as Triples format( RCV format) and Compressed Sparse Row format (CSR). The implementations in Java to convert sparse Matrix to RCV & CSR Formats are explained in interactive mode. This course also covers applying sparse matrix in one of the machine learning applications “sentiment analysis on products”. Sparse matrix operations are explained in step by step with suitable examples. Operations included are Addition, Transpose and Multiplication. All the operations are explained with step by step process of rows(Row ,Column and Value) of input matrices. Movement of position from one row of a matrix to another row is explained in a simple way. The entire course is taught with Java coding. Learners feel more easier to understand the coding and influences to write their own coding.
Who this course is for:
- Under graduate & Post graduate
- Research Scholars
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