[ FreeCourseWeb ] Udemy - 2021 Linear Algebra for Machine Learning

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[ FreeCourseWeb.com ] Udemy - 2021 Linear Algebra for Machine Learning
  • Get Bonus Downloads Here.url (0.2 KB)
  • ~Get Your Files Here ! 1. Introduction
    • 1. Introduction-en_US.srt (4.3 KB)
    • 1. Introduction.mp4 (20.3 MB)
    10. Outro
    • 1. Until Next Time-en_US.srt (1.2 KB)
    • 1. Until Next Time.mp4 (4.1 MB)
    2. Vectors and the Manipulation of Vectors
    • 1. Vectors-en_US.srt (6.2 KB)
    • 1. Vectors.mp4 (19.3 MB)
    • 2. Systems of Linear Equations-en_US.srt (13.2 KB)
    • 2. Systems of Linear Equations.mp4 (35.2 MB)
    3. Matrices
    • 1. Introduction to Matrices-en_US.srt (3.7 KB)
    • 1. Introduction to Matrices.mp4 (15.7 MB)
    • 2. Matrix Addition and Multiplication-en_US.srt (8.1 KB)
    • 2. Matrix Addition and Multiplication.mp4 (27.0 MB)
    • 3. Properties of Matrices-en_US.srt (8.3 KB)
    • 3. Properties of Matrices.mp4 (30.9 MB)
    4. Systems of Linear Equations and Matrices
    • 1. Solving Systems of Linear Equations-en_US.srt (9.0 KB)
    • 1. Solving Systems of Linear Equations.mp4 (35.0 MB)
    • 2. Gaussian Elimination-en_US.srt (5.7 KB)
    • 2. Gaussian Elimination.mp4 (18.6 MB)
    • 3. Gaussian Elimination Continued-en_US.srt (9.0 KB)
    • 3. Gaussian Elimination Continued.mp4 (32.5 MB)
    • 4. Solving for the Inverse-en_US.srt (6.2 KB)
    • 4. Solving for the Inverse.mp4 (20.5 MB)
    5. Introduction to Vector Spaces
    • 1. Vector Spaces-en_US.srt (5.8 KB)
    • 1. Vector Spaces.mp4 (22.1 MB)
    • 2. Definition of a Vector-en_US.srt (4.1 KB)
    • 2. Definition of a Vector.mp4 (16.6 MB)
    6. Linear Independence
    • 1. Introduction to Linear Independence-en_US.srt (4.6 KB)
    • 1. Introduction to Linear Independence.mp4 (19.4 MB)
    • 2. Gaussian Elimination and Linear Independence-en_US.srt (3.1 KB)
    • 2. Gaussian Elimination and Linear Independence.mp4 (7.3 MB)
    • 3. Introduction of the Basis-en_US.srt (4.3 KB)
    • 3. Introduction of the Basis.mp4 (16.4 MB)
    • 4. Gaussian and the Basis-en_US.srt (3.4 KB)
    • 4. Gaussian and the Basis.mp4 (10.2 MB)
    • 5. Rank-en_US.srt (3.6 KB)
    • 5. Rank.mp4 (14.7 MB)
    7. Linear Mappings
    • 1. Introduction to Linear Mappings-en_US.srt (5.3 KB)
    • 1. Introduction to Linear Mappings.mp4 (20.2 MB)
    • 2. Matrix representation of Linear Mappings-en_US.srt (6.4 KB)
    • 2. Matrix representation of Linear Mappings.mp4 (25.9 MB)
    • 3. Basis Change-en_US.srt (3.9 KB)
    • 3. Basis Change.mp4 (13.7 MB)
    • 4. Basis Change Theorem-en_US.srt (5.7 KB)
    • 4. Basis Change Theorem.mp4 (29.7 MB)
    • 5. Image and Kernel-en_US.srt (8.0 KB)
    • 5. Image and Kernel.mp4 (34.3 MB)
    • 6. Affine Spaces-en_US.srt (6.5 KB)
    • 6. Affine Spaces.mp4 (28.7 MB)
    8. Linear Algebra and Analytic Geometry
    • 1. Norm-en_US.srt (4.3 KB)
    • 1. Norm.mp4 (11.9 MB)
    • 2. Inner Product-en_US.srt (4.1 KB)
    • 2. Inner Product.mp4 (15.0 MB)
    • 3. Positive Definite Matrix and Symmetry-en_US.srt (6.0 KB)
    • 3. Positive Definite Matrix and Symmetry.mp4 (25.4 MB)
    • 4. Lengths and Distances-en_US.srt (3.8 KB)
    • 4. Lengths and Distances.mp4 (12.9 MB)
    • 5. Angles and Orthogonality-en_US.srt (5.9 KB)
    • 5. Angles and Orthogonality.mp4 (23.3 MB)
    • 6. Orthonormal Basis-en_US.srt (4.3 KB)
    • 6. Orthonormal Basis.mp4 (17.2 MB)
    • 7. Inner Products of Functions-en_US.srt (3.5 KB)
    • 7. Inner Products of Functions.mp4 (9.1 MB)
    9. Projections
    • 1. Introduction to Orthogonal Projections-en_US.srt (2.6 KB)
    • 1. Introduction to Orthogonal Projections.mp4 (7.4 MB)
    • 2. Projections onto one-dimensional subspaces-en_US.srt (8.6 KB)
    • 2. Projections onto one-dimensional subspaces.mp4 (36.3 MB)
    • 3. Projections onto general subspaces-en_US.srt (12.2 KB)
    • 3. Projections onto general subspaces.mp4 (50.7 MB)
    • 4. Gram-Schmidt Orthogonalization-en_US.srt (2.8 KB)
    • 4. Gram-Schmidt Orthogonalization.mp4 (9.9 MB)
    • 5. Rotations about the 2nd and 3rd dimensions-en_US.srt (5.3 KB)
    • 5. Rotations about the 2nd and 3rd dimensions.mp4 (16.1 MB)
    • Bonus Resources.txt (0.3 KB)

Description

2021 Linear Algebra for Machine Learning

MP4 | Video: h264, 1280x720 | Audio: AAC, 44100 Hz
Language: English | Size: 753 MB | Duration: 3h 17m
What you'll learn
The applications to Machine Learning
The Fundamentals of Linear Algebra
Operations on a single matrices and multiple matrices
How to perform elementary row operations
Learn how to find the inverse Matrix
Learn how to solve systems of linear equations
Understand matrices as vectors and vector spaces
Will also study Linear combinations and span
As well as subspaces, null-space, basis, standard basis and more
Requirements
Familiarity with secondary-school-level mathematics.
Ability to perform basic mathematical operations on numbers and fractions.
Knowledge of how to solve linear equations.
Understanding of basic algebra concepts.
Description
Good data scientists are familiar with machine learning libraries and algorithms. It is akin to being an amazing pilot of an airplane, with skills that go beyond flying and borders an airplane mechanic. But to be a great data scientist, those skills will have to surpass the mechanics and thus require a greater understanding.

The great data scientist knows how those libraries and algorithms work under the hood. The great data scientist understands the mathematics behind the science. With the speed of technology, there may come a day when the algorithm itself replaces the data scientist. If we look at our original analogy, this would be akin to planes that truly fly themselves.

We are not there yet, but in this scenario the pilot becomes expensive and obsolete. However, the one person who is never obsolete is the engineer who designs the plane or the mechanic who fixes the plane. Linear Algebra is a cornerstone of machine learning. Linear Algebra not only helps improve an intuitive understanding of Machine learning. But Linear Algebra can help the machine learning engineer build better Machine Learning algorithms from Scratch or customize the parameters involved to optimize the algorithms. In this course you will learn about the Linear Algebra behind the Machine Learning Algorithm.

Who this course is for:
Students of Machine Learning
Students of Data Science
Students of Statistical Learning
Students of Linear Algebra
Students of Mathematics

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753.7 MB
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[ FreeCourseWeb ] Udemy - 2021 Linear Algebra for Machine Learning


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