Mathematics for Machine Learning - Linear Algebra
4.0
(5)
34 learners
What you'll learn
This course includes
- 7.5 hours of video
- Certificate of completion
- Access on mobile and TV
Course content
1 modules • 37 lessons • 7.5 hours of video
Mathematics for Machine Learning - Linear Algebra
37 lessons
• 7.5 hours
Mathematics for Machine Learning - Linear Algebra
37 lessons
• 7.5 hours
- M4ML - Linear Algebra - 1.1 Introduction: Solving data science challenges with mathematics 05:55
- M4ML - Linear Algebra - 1.2 Motivations for linear algebra 03:31
- M4ML - Linear Algebra - 1.3 Getting a handle on vectors 09:06
- M4ML - Linear Algebra - 1.4 Operations with vectors 11:29
- M4ML - Linear Algebra - 1.5 Summary 01:06
- M4ML - Linear Algebra - 2.1 Introduction to module 2: Vectors 00:50
- M4ML - Linear Algebra - 2.2 Part 1: Modulus & Inner Product 09:34
- M4ML - Linear Algebra - 2.2 - Part 2: Cosine & Dot Product 05:54
- M4ML - Linear Algebra - 2.2 - Part 3: Projection 06:48
- M4ML - Linear Algebra - 2.3 Changing basis 11:25
- M4ML - Linear Algebra - 2.4 Part 1: Basis, vector space, and linear independence 04:14
- M4ML - Linear Algebra - 2.4 Part 2: Applications of changing basis 03:29
- M4ML - Linear Algebra - 2.5 Summary 01:20
- M4ML - Linear Algebra - 3.1 Matrices, vectors, and solving simultaneous equation problems 05:32
- M4ML - Linear Algebra - 3.2 Part 1: How matrices transform space 05:42
- M4ML - Linear Algebra - 3.2 Part 2: Types of matrix transformation 08:39
- M4ML - Linear Algebra - 3.2 Part 3: Composition or combination of matrix transformations 09:00
- M4ML - Linear Algebra - 3.3 Part 1: Solving the apples and bananas problem: Gaussian elimination 08:01
- M4ML - Linear Algebra - 3.3 Part 2: Going from Gaussian elimination to finding the inverse matrix 08:39
- M4ML - Linear Algebra - 3.4 Determinants and inverses 10:37
- M4ML - Linear Algebra - 3.5 Summary 01:00
- M4Ml - Linear Algebra - 4.1 Einstein summation convention and the symmetry of the dot product 09:54
- M4Ml - Linear Algebra - 4.2 Part 1: Matrices changing basis 11:15
- M4ML - Linear Algebra - 4.2 Part 2: Doing a transformation in a changed basis 04:38
- M4ML - Linear Algebra - 4.3 Orthogonal Matrices 06:41
- M4ML - Linear Algebra - 4.4 The Gram-Schmidt process 06:08
- M4ML - Linear Algebra - 4.5 Example: Reflecting on a plane 14:11
- M4ML - Linear Algebra - 5.1 Welcome to Module 5! 08:15
- M4ML - Linear Algebra - 5.2 What are eigenvalues and eigenvectors? 04:25
- M4ML - Linear Algebra - 5.3 Special eigen-cases 03:33
- M4ML - Linear Algebra - 5.4 Calculating eigenvectors 10:08
- M4ML - Linear Algebra - 5.5 Changing to eigenbasis 05:53
- M4ML - Linear Algebra - 5.6 Eigenbasis example 07:27
- M4ML - Linear Algebra - 5.7 Introduction to PageRank 08:45
- M4ML - Linear Algebra - 5.8 Summary 01:15
- M4ML - Linear Algebra - Wrap up of this Linear Algebra course 01:57
- Mathematics for Machine Learning - Linear Algebra - Full Online Specialism 03:50:40
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