“Complete Linear Algebra for Data Science & Machine Learning” covers every aspect of the subject from start to finish. With over 200 video lessons, each idea is explained in a simple and easytounderstand manner. The course includes tests and assignments with solutions to help you check your understanding. Whether you’re a student, professional, or math enthusiast, this course will guide you through the fundamental concepts of Linear Algebra in a fun and approachable way. In this course, you will learn: Basics of matrices, such as notation, dimensions, types, and addressing entries Operations on a single matrix, including scalar multiplication, transpose, determinant, and adjoint Operations on two matrices, such as addition, subtraction, and multiplication Echelon Forms and performing elementary row operations Inverses, including invertible and singular matrices, and trig functions GaussJordan elimination, determinant properties, and matrices as vectors Vector spaces, including dimensions, Euclidean spaces, closure properties, and axioms Eigenvalues and Eigenvectors, and how to locate the associated Eigenvectors and Eigenvalues Bonus: Concepts in elementary algebra With this course, you will get: Lesson Videos: Watch as the concepts are clearly and concisely taught from scratch Solved Examples: Each topic is taught with the aid of solved examples. Currently, udemy is offering the Complete Linear Algebra for Data Science & Machine Learning for up to 87 % off i.e. INR 449 (INR 3,500).
Who Can Opt for this Course?
 Students who wish to excel in Linear Algebra and are enrolled in the course or who want to enroll
 Professionals who require a math review, particularly in the areas of algebra and linear algebra
 Those who desire to work with linear systems and vector spaces in math, science, and engineering
 Anyone who wants to become proficient in linear algebra for use in computer programming, computer graphics, artificial intelligence, data analysis, machine learning, deep learning, data science, etc
Course Highlights
Key Highlights  Details 

Registration Link  Apply Now! 
Price  INR 449 ( 
Duration  17 Hours 
Rating  4.9/5 
Student Enrollment  4,294 students 
Instructor  Kashif A. https://www.linkedin.com/in/kashifa. 
Topics Covered 

Course Level  N.A 
Total Student Reviews  688 
Learning Outcomes
 How to ace your linear algebra exam and the fundamentals of linear algebra
 Matrices’ fundamentals (notation, dimensions, types, addressing the entries etc)
 Operations, such as scalar multiplication, transposition, determinant, and adjoint, on a single matrix
 Operations using two matrices, such as addition, subtraction, and matrix multiplication
 Finding Echelon Forms and performing simple row operations (REF & RREF)
 Inverses, such as singular and invertible matrices, as well as the Cofactor approach
 Employing matrices and inverse matrices to solve systems of linear equations, using Cramer’s method to solve AX = B
 The characteristics of determinants and GaussJordan elimination
 Matrices can be thought of as vectors, complete with the headtotail rule, components, magnitude, and midpoint
 Dimensions, Euclidean spaces, closure conditions, and axioms for vector spaces
 Span, linear dependence, and linear combinations with span for a vector space
 A matrix’s subspace, null space, and matrixvector products
 Basis and standard basis, as well as determining if a group of provided vectors serves as the foundation of a vector space Identifying Eigenvalues and their related Eigenvectors, as well as how to do so
 Rudimentary algebraic ideas
Course Content
S.No.  Module (Duration)  Topics 

1.  Welcome and Introduction (03 minutes)  Welcome and Introduction 
SOLUTIONS of Assignments  
2.  Basics of Matrices (32 minutes)  Matrices and their Significance – 001 
Matrix Notation – 002  
Dimension (Order) of a Matrix – 003  
Quiz 1: Dimensions of a Matrix  
Addressing Elements of a Matrix – 004  
Quiz 2: Addressing Elements of a Matrix  
Solving Linear Systems in 2 Unknowns – 005  
Quiz 3: Solving Linear Systems in 2 Unknowns  
Solving Linear Systems in 3 Unknowns – 006  
Quiz 4: Solving Linear Systems in 3 Unknowns  
3.  Basics of Matrices (Continued) (01 hour 18 minutes)  IMPORTANT – This section is OPTIONAL 
Types of Matrices  
Addition and Subtraction of Matrices  
Multiplication of Scalars with Matrices  
Multiplication of two Matrices  
Inverse and Determinant of a 2×2 Matrix  
The Formula: Inverse (A) = Adjoint (A) / Determinant (A)  
* EXAMPLE – Inverse of a 2×2 Matrix  
Using Matrices to Solve Simultaneous Linear Equations  
* EXAMPLE – Using Matrices to Solve Simultaneous Linear Equations  
CHALLENGE QUESTION – Using Matrices to Solve Simultaneous Linear Equations  
SUMMARY  
4.  Matrices and Systems of Linear Equations (46 minutes)  The Online Matrix Calculator: A FREE Tool 
Systems of Linear Equations  
Systems of Linear Equations – Continued  
Elementary Row Operations  
Row Echelon Form (REF)  
Reduced Row Echelon Form (RREF)  
* ASSIGNMENT 1: Matrices and Linear Equations  
5.  Matrix Algebra and Operations (21 minutes)  Matrix Algebra – Addition and Subtraction 
Matrix Algebra – Scalar Multiplication  
Matrix Algebra – Matrix Multiplication  
Transpose of a Matrix  
** ASSIGNMENT 2: Matrix Algebra & Operations  
6.  Determinant of a Matrix (20 minutes)  Determinant of a 2×2 Matrix 
Determinant of a 3×3 Matrix  
Finding Determinants Quickly  
*** ASSIGNMENT 3: Computing Determinants  
7.  Inverse of a Matrix (46 minutes)  Inverse exists only for Square Matrices 
Singular Matrices  
Importance of Inverse in solving Linear Systems  
Inverse of a 2×2 Matrix  
Inverse of a 3×3 Matrix – The Two Methods  
Inverse of a 3×3 Matrix – The Cofactor Method  
Inverse of a 3×3 Matrix – GaussJordan Elimination Method  
8.  Properties of Determinants (14 minutes)  Properties of Determinants – Row Operation 1 
Properties of Determinants – Row Operation 2  
Properties of Determinants – Row Operation 3  
Properties of Determinants – All Row Operations  
Properties of Determinants – Row Operations Applied  
Properties of Determinants – Another Property  
9.  *** OPTIONAL: Introduction to Vectors (52 minutes)  Introduction to the Section 
Scalars and Vectors  
Geometrical Representation of Vectors  
Vector Addition and Subtraction  
Laws of Vector Addition and Head to Tail Rule  
Unit Vector  
Components of a Vector in 2D  
Position Vector  
3D Vectors and Magnitude of a Vector  
Displacement Vector  
Finding Midpoint using Vectors  
10.  Vector Spaces (40 minutes)  Introduction to Vector Spaces 
Euclidean Vector Spaces – Part 1  
Euclidean Vector Spaces – Part 2  
Euclidean Vector Spaces – Part 3  
Definition and Closure Properties  
Axioms of Vector Spaces  
Example of Closure Properties  
Example 1 of Vector Spaces  
Example 2 of Vector Spaces  
11.  Subspace and Nullspace (24 minutes)  Subspaces – Introduction 
Subspaces – Example  
Subspaces – Example 2  
Subspaces – Example 3  
Nullspace of a Matrix  
Nullspace of a Matrix – Example  
**** ASSIGNMENT 4: Vector Spaces, Subspaces and Null Spaces  
12.  Span and Spanning Sets (20 minutes)  Span of a set of vectors 
Span of a set of vectors – Example  
Spanning Set for a Vector Space – Introduction and Examples  
Spanning Set – Example 3  
Spanning Set – Example 4  
13.  Linear Dependence and Independence (31 minutes)  Linear Dependence – Introduction 
Linear Dependence – Definition  
Linear Dependence – Examples  
Linear Dependence – More Examples  
Linear Dependence – A faster method to check dependency  
Linear Dependence – If X is not a Square Matrix  
Linear Dependence – Example of a Nonsquare X Matrix  
14.  Basis and Dimension (17 minutes)  Basis – Definition and Example 
Basis – Another Example  
Basis – Dimension of a Vector Space  
Basis – Example of Dimension of a Vector Space  
Basis – Standard Basis  
***** ASSIGNMENT 5: Span, Linear Independence and Basis  
15.  Eigenvalues and Eigenvectors (22 minutes)  Introduction to Eigenvalues and Eigenvectors 
How to Calculate Eigenvalues and Eigenvectors  
EXAMPLE: Calculating Eigenvalues and Eigenvectors of a 2×2 Matrix  
16.  Basic Algebra Concepts (Additional Lessons) (04 hours 41 minutes)  Mathematical Operators and their Precedence (BODMAS) 
Power and Roots  
Rounding and Estimation  
Rounding with Decimal Places  
Rounding with Significant Figures  
Estimating  
Fractions, Decimals and Percentages  
Ratio and Proportion  
Introduction to the Number System  
Natural Numbers  
Whole Numbers  
Integers  
Rational Numbers  
Irrational Numbers  
EXERCISES  
Real Numbers  
Venn Diagram and Flowchart  
EXAMPLES 1  
EXAMPLES 2  
EXAMPLE 3  
COORDINATE GEOMETRY & STRAIGHT LINES  
The Coordinate System  
The Coordinate System – continued  
Length of a Line Segment  
EXAMPLES: Length of a Line Segment  
EXERCISE: Length of a Line Segment  
EXAMPLE: Midpoint of a Line Segment  
EXERCISE: Midpoint of a Line Segment  
SUMMARY  
Equation of a Straight Line  
Gradient of a Straight Line  
EXAMPLE 1: Equation of a Straight Line  
EXAMPLE 2: Equation of a Straight Line  
EXAMPLE 3: Equation of a Straight Line  
EXERCISES: Equation of a Straight Line  
Other Forms of Linear Equations  
A Second Formula for a Straight Line  
EXERCISE  
SUMMARY  
Straight Lines  
Intersection of Two Lines  
EXAMPLE: Intersection of Two Lines  
EXERCISE: Intersection of Two Lines  
ACTIVITY 1: Straight Lines  
ACTIVITY 2: Straight Lines  
Parallel and Perpendicular Lines  
EXAMPLES: Parallel and Perpendicular Lines  
EXERCISES: Parallel and Perpendicular Lines  
SUMMARY  
Udemy Supplement LA  
FUNCTIONS, GRAPHS & TRANSFORMATIONS  
Functions  
Graphs of Common Functions  
Straight Line  
Graph of a Linear Function  
Validation of Graphs as Functions  
Library of Functions  
Asymptotes  
Limits  
ACTIVITY: Memorize these Graphs  
Translations – Vertical Shift  
Translations – Horizontal Shift  
ACTIVITY: Translations  
SUMMARY – Translations  
EXERCISE: Translations  
Transformations – Stretching and Compression  
ACTIVITY: Transformations  
EXERCISE: Transformations  
SUMMARY: Stretching and Compression  
SUMMARY: Translation and Transformation  
Reflection about Xaxis and Yaxis  
SUMMARY: Reflections  
EXERCISE: Sketching Complex Graphs  
SUMMARY  
Common Graphs  
Basic Types of Graph Manipulations1  
Basic Types of Graph Manipulations2  
EXAMPLE: Manipulation of Graphs  
17.  Congratulations and Bonus Material (05 hours 18 minutes)  Congratulations and Thank You! 
Bonus Material: Getting free Trial of Software to create Whiteboard Animations  
Overview of the Project Screen  
Customizing the Default Settings  
Creating a New Project  
Brief Overview of the Tools and Saving the Project  
Changing the Default Drawing Hand  
Canvas Color and Texture  
Adding the First Image and Adjusting it  
Image Properties  
Adding Text  
Exporting Your Video (and more Text Properties)  
Project 1 – Solution, and Adding Background Music  
Solution and adding Tracks – P1soln – WBA  
Camera Settings  
More Camera Settings and Creating a New Scene  
Timeline and Relocating Copied Elements  
Drawing Without Hand Leaving the Screen, and Native Elements  
Move In Effect  
Project 2 – Solution Part 1  
Project 2 – Solution Part 2  
Charts and their Types  
Importing Charts from Microsoft Excel etc.  
The Erase Effect  
Graphic Enhancements and Filter Effects  
Project 3 – Solution  
GIF Files in VideoScribe  
Making your own GIF files and Importing to VideoScribe  
Drawing Bitmap and JPEG Images in VideoScribe  
Writing Text in Languages Not Supported by VideoScribe  
The Morph Effect  
Handbrake Software to Compress Exported Videos Without Losing Quality  
Project 4 – Solution Part 1  
Project 4 – Solution Part 2  
Project 5 – Solution and Recording Voice Over  
BONUS Lecture: Get Any of Kashif’s Courses for Up to 95% Off  
Royalty Free Resources 
Resources Required
 A deep desire to understand vectors and matrices Possess the ability to manipulate integers and fractions using basic mathematical operations (+, , x, )
 Understanding of how to calculate a linear equation, like as 3x4=11
 Understanding of fundamental algebraic principles, such as powers and roots, factoring, simplifying fractions, solving equations, and graphing
 To take this course, you only need to be familiar with basic math and algebra
 The majority of the aforementioned prerequisite subjects are covered in the course, which is the finest part
Featured Review
Amy Bourque (5/5) : I use this class to supplement the very quick linear algebra section in a data science bootcamp. It was the perfect accompaniment to help me understand linear algebra in order to visualize and comprehend how it applies to data science.
Pros
 John Ross (5/5) : Everything is very well explained and going at an excellent speed for a beginner.
 Kuro Saki (5/5) : Amazing course for anybody who want to get into data science and AI since Linear Algebra is very important for that.
 Sharon Moak (5/5) : So far the pacing is perfect and the explanations are thorough.
 Om Khatri (5/5) : This is by far the best course linear algebra course out there!
Cons
 Eliakin (2/5) : – The course is not really intended for individuals who want to “master Linear Algebra for Data Science, Data Analysis, Artificial Intelligence, Machine Learning, Deep Learning, Computer Graphics, Programming, etc.” as advertised.
 Eliakin (2/5) : I am disappointed at 1) all the reviewers who made it seem like this course was an outstanding investment and 2) at the false advertisement.
About the Author
The instructor of this course is Kashif A. who is a Bestselling Instructor. With 4.5 Instructor Rating and 5,588 Reviews on Udemy, he/she offers 13 Courses and has taught 81,265 Students so far.
 Kashif graduated with a Master in Engineering from one of the best US universities With 11 years of experience instructing at the collegiate level, he enjoys teaching
 He is keen about online education and digital entrepreneurship in addition to traditional academics
 Motion graphics and photo and video editing are areas of interest for him
 Kashif enjoys travelling, cooking, and reading in his free time
Comparison Table
Parameters  Complete Linear Algebra for Data Science & Machine Learning  Complete linear algebra: theory and implementation in code  Mathematical Foundations of Machine Learning 

Offers  INR 455 (  INR 455 (  INR 455 ( 
Duration  18 hours  34 hours  16.5 hours 
Rating  4.9 /5  4.9 /5  4.6 /5 
Student Enrollments  4,294  27,847  102,492 
Instructors  Kashif A.  Mike X Cohen  Dr Jon Krohn 
Register Here  Apply Now!  Apply Now!  Apply Now! 
Leave feedback about this