About this Course

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Learner Career Outcomes

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got a tangible career benefit from this course
Shareable Certificate
Earn a Certificate upon completion
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Start instantly and learn at your own schedule.
Flexible deadlines
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Beginner Level
Approx. 19 hours to complete
English

Skills you will gain

Eigenvalues And EigenvectorsBasis (Linear Algebra)Transformation MatrixLinear Algebra

Learner Career Outcomes

29%

started a new career after completing these courses

29%

got a tangible career benefit from this course
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Beginner Level
Approx. 19 hours to complete
English

Offered by

Placeholder

Imperial College London

Syllabus - What you will learn from this course

Content RatingThumbs Up92%(32,632 ratings)Info
Week
1

Week 1

2 hours to complete

Introduction to Linear Algebra and to Mathematics for Machine Learning

2 hours to complete
5 videos (Total 28 min), 4 readings, 3 quizzes
5 videos
Motivations for linear algebra3m
Getting a handle on vectors9m
Operations with vectors11m
Summary1m
4 readings
About Imperial College & the team5m
How to be successful in this course5m
Grading policy5m
Additional readings & helpful references10m
3 practice exercises
Exploring parameter space20m
Solving some simultaneous equations15m
Doing some vector operations30m
Week
2

Week 2

2 hours to complete

Vectors are objects that move around space

2 hours to complete
8 videos (Total 44 min)
8 videos
Modulus & inner product10m
Cosine & dot product5m
Projection6m
Changing basis11m
Basis, vector space, and linear independence4m
Applications of changing basis3m
Summary1m
4 practice exercises
Dot product of vectors15m
Changing basis15m
Linear dependency of a set of vectors15m
Vector operations assessment15m
Week
3

Week 3

3 hours to complete

Matrices in Linear Algebra: Objects that operate on Vectors

3 hours to complete
8 videos (Total 57 min)
8 videos
How matrices transform space5m
Types of matrix transformation8m
Composition or combination of matrix transformations8m
Solving the apples and bananas problem: Gaussian elimination8m
Going from Gaussian elimination to finding the inverse matrix8m
Determinants and inverses10m
Summary59s
2 practice exercises
Using matrices to make transformations30m
Solving linear equations using the inverse matrix30m
Week
4

Week 4

7 hours to complete

Matrices make linear mappings

7 hours to complete
6 videos (Total 53 min)
6 videos
Matrices changing basis11m
Doing a transformation in a changed basis4m
Orthogonal matrices6m
The Gram–Schmidt process6m
Example: Reflecting in a plane14m
2 practice exercises
Non-square matrix multiplication20m
Example: Using non-square matrices to do a projection30m

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About the Mathematics for Machine Learning Specialization

Mathematics for Machine Learning

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