Linear Algebra - Complete Guide
2.1 (6 ratings)
3,008 students enrolled

Linear Algebra - Complete Guide

Learn vector spaces , abstract vector spaces , linear transformations, inner product, orthogonality, cross product etc
2.1 (6 ratings)
3,008 students enrolled
Created by Up Degree
Last updated 6/2018
English
English [Auto]
Current price: \$135.99 Original price: \$194.99 Discount: 30% off
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This course includes
• 3.5 hours on-demand video
• Access on mobile and TV
• Certificate of Completion
Training 5 or more people?

What you'll learn
• In depth Concept about Linear Algebra
Requirements
• Basic Algebra Knowledge
Description

This course gives you a broad overview on several concepts and practice problems of Algebra. If you want to learn Algebra concepts from scratch and become an Algebra master, this course is for you!

If you're looking to gain a solid foundation in Linear Algebra, allowing you to study on your own schedule at a fraction of the cost it would take at a traditional university, to further your career goals, this online course is for you

The course aims to introduce

• Real n- dimensional vector spaces,
• Abstract vector spaces and their axioms,
• Linear Transformations,
• Inner Product,
• Orthogonality,
• Cross product and their geometric applications,
• Subspaces,
• Lines Independence,
• Bases for Vector Spaces,
• Dimension,
• Matrix Rank,
• Eigen Vectors,
• Eigen Values,
• Matrix Diagonalization
Who this course is for:
• High School Students
• College Students
Course content
Expand all 26 lectures 03:24:47
+ Vector spaces & Linear Transformation
8 lectures 52:48
Preview 08:57
Preview 08:23
Linear Transformations
04:46
Rank-nullity Theorem
06:24
Matrix Representation of a Linear Transformation
07:39
Dual Spaces
06:32
+ Application: Differential equations
3 lectures 22:26
Superposition Principle and Wronskian
05:37
Second Order Differential Equation with Constant Coefficient
07:34
Fundamental set of Solutions
09:15
+ Linear Operator
4 lectures 29:55
Eigen values & Eigen vectors of linear Operators
05:41
Diagonalization
08:30
Markov Chain
06:29
Preview 09:15
+ Inner product spaces
6 lectures 53:42
Inner product spaces
09:52
The dot product on Rn
08:52
Orthonormal Bases
07:53
Orthogonal Complements
09:53
Diagonalization of Symmetric Matrices
09:41
Least Square Apploximation
07:31