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.
Fundamentals to Linear Algebra
20h 53m 19s
English
Paid
About the Author: LunarTech
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.
Watch Online 35 lessons
0:00
/ #1: Welcome Message
All Course Lessons (35)
| # | Lesson Title | Duration | Access |
|---|---|---|---|
| 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 |
Unlock unlimited learning
Get instant access to all 34 lessons in this course, plus thousands of other premium courses. One subscription, unlimited knowledge.
Learn more about subscriptionRelated courses
-
Updated 1y agoStatistics Fundamentals
By: LunarTechMaster statistics for data-driven careers. Build a strong statistical foundation for data science, analysis, and decision making.2h 4m5/5 -
Updated 6mo agoGrokking Statistics
By: Thomas NieldDiscover statistics with clear explanations and real-life examples. Learn probability, distributions, and hypothesis testing for practical applications. -
Updated 2y agoMacroeconomics Made Clear
By: Wondrium (The Great Courses), Akila WeerapanaIn 24 revealing lectures, Macroeconomics Made Clear will introduce you to the subject that people have been theorizing about and modeling for almost 100 years.12h 34m
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.