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Machine Learning | Jon Krohn

Jon Krohn

Jon Krohn

Dr. Jon Krohn wrote the bestselling book Deep Learning Illustrated and hosts the world's most listened-to data science podcast. He's renowned for his compelling lectures, which he offers in-person at Columbia University, New York University, and leading conferences like Web Summit and Collision.. To keep up with the latest from Jon, follow him on LinkedIn (linkedin.com/in/jonkrohn) and sign up for his newsletter at jonkrohn.com.

Course Details

  • Course Lessons48
  • Course Period6h 57m
  • No.Students4
  • 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