Linear algebra is not only important, but is essential in solving problems in Data Science and Machine learning, and the applications of this field are ranging from mathematical applications to newfound technologies like computer vision, NLP (Natural Language processing), etc. Because essentially Linear Algebra could be considered as the fundamental block of Data Science. So, if you are strong in your basics, you could make better models that are efficient and perform better than the models developed without a deep understanding of these fundamental blocks. Some of the most important linear algebra topics that are important in the field of data science are PCA (Principal Component Analysis, Singular value decomposition (SVD), probability and statistics, LU decomposition, Eigen decomposition, QR decomposition, etc. if you wish to get started with Data Science, then I would recommend you to enroll in a Data Science training course from Intellipaat. Also, have a look at YouTube videos on Data Science courses from our experts.
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