Machine Learning for Engineering & Science Applications | IIT Madras - #90 PCA | Part 2 | Machine Learning for Engineering & Science Applications
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What you'll learn
Understand the historical development and foundational concepts of artificial intelligence.
Gain proficiency in applying machine learning techniques to engineering and science problems.
Develop skills in using linear algebra and calculus for machine learning modeling.
Learn to implement and optimize machine learning algorithms using Python packages.
Welcome to 'Machine Learning for Engineering & Science Applications' course !
This lecture provides a more detailed explanation of PCA. PCA finds the principal components of a dataset, which are the directions of greatest variance. The principal components are the eigenvectors of the covariance matrix of the data.
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#PCAPrincipalComponentAnalysis #DimensionalityReduction #Variance #Eigenvectors #Eigenvalues
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