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Fundamentals to Linear Algebra

20h 53m 19s
English
Paid

Fundamentals to Linear Algebra is a 35-lesson 20 hours 53 minutes self-paced course by LunarTech. Unleash the power of linear algebra to conquer the world of data science, machine learning, and artificial intelligence.

Course facts

Lessons
35
Duration
20 hours 53 minutes
Level
All levels
Language
English
Updated
Instructor
LunarTech
Price
Premium

Unleash the power of linear algebra to conquer the world of data science, machine learning, and artificial intelligence. This intensive course will transform you into a master of mathematics, ready to tackle real-world challenges with practical skills and unwavering confidence.

Who teaches Fundamentals to Linear Algebra? LunarTech

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LunarTech is an online tech academy focused on data science, machine learning, and quantitative analysis — covering both the theoretical foundations (linear algebra, calculus, statistics) and the practical Python / SQL toolchain that working data scientists use. The school operates globally with cohort-based and self-paced tracks.

The CourseFlix listing carries twelve LunarTech courses spanning machine-learning theory, deep learning, applied data-science workflows, and the math fundamentals underlying the field. Material is paid and aimed at engineers and analysts transitioning into formal data-science roles or upskilling within them.

What lessons are included in Fundamentals to Linear Algebra?

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#1: Welcome Message
All Course Lessons (35)
#Lesson TitleDurationAccess
1
Welcome Message Demo
14:18
2
Linear Algebra RoadMap 2024
19:12
3
Pre-Requisites Introduction
09:03
4
Refreshment - Norms & Euclidean Distance
10:31
5
Refreshment - Real Numbers and Vector Space
04:14
6
Refreshment - Cartesian Coordinate System & Unit Circle
04:45
7
Refreshment - Angles, Unit Circle and Trigonometry
13:24
8
Refreshment - Pythagorean Theorem & Orthogonality
05:25
9
Why these Pre-Requisites Matter
02:28
10
Module 2.1: Foundations of Vectors
30:55
11
Module 2.2: Special Vectors and Operations
56:53
12
Module 2.3: Part 1 - Scalar Multiplication
25:06
13
Module 2.3 Part 2 - Linear Combination and Unit Vectors
47:10
14
Module 2.3 Part 3 - Span of Vectors
40:06
15
Module 2.3: Part 4 - Linear Independence
31:53
16
Module 2.4: Dot Product, Cauchy-Schwarz Inequality and Its
01:29:45
17
Module 1: Foundations of Linear Systems and Matrices
16:50
18
Module 2: Introduction to Matrices
30:01
19
Module 3: Core Matrix Operations
35:01
20
Module 4: Part 1 Solving Linear Systems - Gaussian Reduction
47:23
21
Module 4: Part 2 Solving Linear Systems - Gaussian Reduction
01:07:47
22
Module 4: Part 3 Solving Linear Systems - Gaussian Reduction
01:10:00
23
Module 4: Part 4 Solving Linear Systems - Gaussian Reduction
53:47
24
Module 1: Algebraic Laws for Matrices
56:31
25
Module 2: Determinants and Their Properties
50:52
26
Module 3: Matrix Inverses and Identity Matrix
01:03:32
27
Module 4: Transpose of Matrices: Properties and Applications
23:15
28
Module 1: Part 1 Basis of Vector Space
35:39
29
Module 1: Part 2 Vector Projection and Calculation
41:12
30
Module 1: Part 3 Gram-Schmidt Process
36:53
31
Module 2: Special Matrices and Their Properties
14:39
32
Module 3: Matrix Factorization, Examples and Applications
27:03
33
Module 4: QR Decomposition Overview
42:52
34
Module 5: Eigenvalues, Eigenvectors, and Eigen Decomposition
01:16:31
35
Module 6: Singular Value Decomposition (SVD)
58:23
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Frequently asked questions

What prerequisites are necessary before taking this course?
Before enrolling in the course, students are expected to have a solid foundation in basic mathematics, including knowledge of real numbers, vector spaces, and the Cartesian coordinate system. The course includes a refreshment on these topics, covering norms, Euclidean distance, and trigonometry, among others, to ensure all participants are adequately prepared.
What projects or practical applications will be covered in this course?
The course focuses on practical skills through projects involving solving linear systems using Gaussian reduction and understanding matrix operations. Students will also engage in exercises involving vector space basis, vector projection, and the application of the Gram-Schmidt process, which are essential in data science and machine learning applications.
Who is the target audience for this course?
The course is designed for individuals interested in data science, machine learning, and artificial intelligence who wish to deepen their understanding of linear algebra. It is suitable for both students and professionals looking to apply mathematical concepts in real-world scenarios.
How does this course compare to other linear algebra courses in terms of depth and scope?
This course offers a comprehensive exploration of linear algebra, covering foundational topics such as vectors, matrices, determinants, and matrix factorization. It delves into advanced concepts like eigenvalues, eigenvectors, and singular value decomposition, providing a broad scope suitable for applications in data science.
What specific tools or platforms will I learn to use in this course?
While the course focuses primarily on theoretical concepts, students will engage with practical exercises that involve matrix operations, vector calculations, and solving linear systems, which are commonly used in various mathematical and computational software environments.
What topics are not covered in this course?
The course does not cover advanced data science techniques or specific programming languages for implementing linear algebra in computational environments. It focuses on mathematical concepts and their theoretical applications rather than programming or software-specific skills.
How much time should I expect to commit to this course?
The course consists of 35 lessons, and students should anticipate dedicating a significant amount of time to fully understand and practice the concepts presented. The exact amount of time required will depend on the student's prior knowledge and pace of study.