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Machine Learning | Jon Krohn

Jon Krohn

Jon Krohn

Dr. Jon Krohn is Chief Data Scientist at Nebula, author of the bestselling book Deep Learning Illustrated, and host of the SuperDataScience podcast. Jon is renowned for his compelling lectures, which he offers in-person at Columbia University, New York University, and leading industry conferences.. Copies of Deep Learning Illustrated are available at bit.ly/iTkrohn. Use KROHN during checkout for 35% off!. Also available from Amazon at amzn.to/32TB6rB. To keep up with the latest from Jon, s

Course Details

  • Course Lessons48
  • Course Period6h 57m
  • No.Students1
  • LanguageEnglish
  • No Prerequisite
  • (1)
  • Start Now for free

Course Lessons

  1. 1 | Machine Learning Foundations: Welcome to the Journey 00:02:38
  2. 2 | What Linear Algebra Is — Topic 1 of Machine Learning Foundations 00:24:04
  3. 3 | Plotting a System of Linear Equations — Machine Learning Foundations Bonus Video 00:09:19
  4. 4 | Linear Algebra Exercise — Topic 2 of Machine Learning Foundations 00:02:05
  5. 5 | Tensors — Topic 3 of Machine Learning Foundations 00:02:34
  6. 6 | Scalars — Topic 4 of Machine Learning Foundations 00:13:05
  7. 7 | Vectors and Vector Transposition — Topic 5 of Machine Learning Foundations 00:12:19
  8. 8 | Norms and Unit Vectors — Topic 6 of Machine Learning Foundations 00:15:10
  9. 9 | Basis, Orthogonal, and Orthonormal Vectors — Topic 7 of Machine Learning Foundations 00:04:30
  10. 10 | Matrix Tensors — Topic 8 of Machine Learning Foundations 00:08:24
  11. 11 | Generic Tensor Notation — Topic 9 of Machine Learning Foundations 00:06:44
  12. 12 | Exercises on Algebra Data Structures — Topic 10 of Machine Learning Foundations 00:00:42
  13. 13 | Tensor Operations — Segment 2 of Subject 1, "Intro to Linear Algebra", ML Foundations 00:01:20
  14. 14 | Tensor Transposition — Topic 11 of Machine Learning Foundations 00:03:53
  15. 15 | Basic Tensor Arithmetic (The Hadamard Product) — Topic 12 of Machine Learning Foundations 00:06:13
  16. 16 | Tensor Reduction — Topic 13 of Machine Learning Foundations 00:03:32
  17. 17 | The Dot Product — Topic 14 of Machine Learning Foundations 00:05:14
  18. 18 | Exercises on Tensor Operations — Topic 15 of Machine Learning Foundations 00:00:57
  19. 19 | Solving Linear Systems with Substitution — Topic 16 of Machine Learning Foundations 00:04:04
  20. 20 | Solving Linear Systems with Elimination — Topic 17 of Machine Learning Foundations 00:05:52
  21. 21 | Visualizing Linear Systems — Machine Learning Foundations Bonus Video 00:10:59
  22. 22 | Matrix Properties — Final Segment of Subject 1, "Intro to Linear Algebra", ML Foundations 00:02:06
  23. 23 | The Frobenius Norm — Topic 18 of Machine Learning Foundations 00:05:02
  24. 24 | Matrix Multiplication — Topic 19 of Machine Learning Foundations 00:25:00
  25. 25 | Symmetric and Identity Matrices — Topic 20 of Machine Learning Foundations 00:04:42
  26. 26 | Matrix Multiplication Exercises — Topic 21 of Machine Learning Foundations 00:00:52
  27. 27 | Matrix Inversion — Topic 22 of Machine Learning Foundations 00:17:07
  28. 28 | Diagonal Matrices — Topic 23 of Machine Learning Foundations 00:03:26
  29. 29 | Orthogonal Matrices — Topic 24 of Machine Learning Foundations 00:05:50
  30. 30 | Orthogonal Matrix Exercises — Topic 25 of Machine Learning Foundations 00:02:11
  31. 31 | Linear Algebra II: Matrix Operations — Subject 2 of Machine Learning Foundations 00:17:53
  32. 32 | Applying Matrices — Topic 26 of Machine Learning Foundations 00:07:32
  33. 33 | Affine Transformations — Topic 27 of Machine Learning Foundations 00:18:53
  34. 34 | Eigenvectors and Eigenvalues — Topic 28 of Machine Learning Foundations 00:26:47
  35. 35 | Matrix Determinants — Topic 29 of Machine Learning Foundations 00:08:05
  36. 36 | Determinants of Larger Matrices — Topic 30 of Machine Learning Foundations 00:08:42
  37. 37 | Determinant Exercises — Topic 31 of Machine Learning Foundations 00:01:28
  38. 38 | Determinants and Eigenvalues — Topic 32 of Machine Learning Foundations 00:16:16
  39. 39 | Eigendecomposition — Topic 33 of Machine Learning Foundations 00:12:49
  40. 40 | Eigenvector and Eigenvalue Applications — Topic 34 of Machine Learning Foundations 00:13:02
  41. 41 | Matrix Operations for Machine Learning — Final Segment of Subject 2, "Linear Algebra II" 00:03:22
  42. 42 | Singular Value Decomposition — Topic 35 of Machine Learning Foundations 00:10:50
  43. 43 | Data Compression with SVD — Topic 36 of Machine Learning Foundations 00:11:33
  44. 44 | The Moore-Penrose Pseudoinverse — Topic 37 of Machine Learning Foundations 00:12:23
  45. 45 | Regression with the Pseudoinverse — Topic 38 of Machine Learning Foundations 00:18:57
  46. 46 | The Trace Operator — Topic 39 of Machine Learning Foundations 00:04:37
  47. 47 | Principal Component Analysis (PCA) — Topic 40 of Machine Learning Foundations 00:08:27
  48. 48 | Linear Algebra Resources — Topic 41 of Machine Learning Foundations 00:06:11
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    29-07-2024
    Linear Algebra for Machine Learning

    This is a complete course on linear algebra for machine learning. It is also the first quarter of my broader ML Foundations series, which details all of the foundational subjects -- linear algebra, calculus, statistics, and computer science -- that underlie contemporary ML and data science techniques.
    A detailed curriculum for this "Linear Algebra for ML" course is available at jonkrohn.com/LA4ML
    More detail about my entire ML Foundations series and all of the associated open-source Python code is available at github.com/jonkrohn/ML-foundations