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Exercises on Algebra Data Structures — Topic 10 of Machine Learning Foundations

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In this quick video from my Machine Learning Foundations series, I present three questions to test your comprehension of the Linear Algebra concepts introduced in the preceding handful of videos.
Exercises on Algebra Data Structures — Topic 10 of Machine Learning Foundations
Linear algebra for data science, chapter 6 exercise 6 (addition rule of matrix rank)
Linear algebra for data science, chapter 7 exercise 7 (image feature extraction)
Linear algebra for data science, chapter 5 exercise 7 (matrix order of operations LIVE EVIL)
Linear algebra for data science, chapter 10 exercise 1 (timing the LU decomposition)
Linear algebra for data science, chapter 5 exercise 8 (check for matrix symmetry)
Linear algebra for data science, chapter 6 exercise 7 (visualizing the rules of matrix rank)
Linear algebra for data science, chapter 9 exercise 7 (properties of R for tall A)
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Linear algebra for data science, chapter 9 exercise 6 (norms of Q matrices)
Linear algebra for data science, chapter 2 exercise 8 (orthogonal projection)
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Linear algebra for data science, chapter 6 exercise 1 (matrix norm and scalar mult.)
Linear algebra for data science, chapter 8 exercise 8 (geometry of the matrix inverse)
Linear algebra for data science, chapter 10 exercise 5 (AtA, LU, and permutation matrices)
Linear algebra for data science, chapter 5 exercise 6 (matrix multiplication in for-loops)
Linear algebra for data science, chapter 5 exercise 2 (slicing submatrices)
Linear algebra for data science, chapter 15 exercise 5 (create and visualize data for LDA)
Linear algebra for data science, chapter 13 exercise 8 (random data with specified correlations)
Linear algebra for data science, chapter 15 exercise 8 (implement LDA using sklearn)
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Linear algebra for data science, chapter 3 exercise 1 (linear weighted combination)
Linear algebra for data science, chapter 7 exercise 1 (covariance to correlation)
Linear algebra for data science, chapter 4 exercise 4 (correlation computation times)
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