English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 35 Lessons (20h 53m) | 2.33 GB
Unlock the power of linear algebra to conquer data science, machine learning, and AI. This intensive course transforms you into a math ninja, ready to tackle real-world challenges with practical skills and unshakeable confidence.
You’ll learn
Ready to Ace Linear Algebra Exam
Equip yourself to excel in academic examinations with a deep understanding of linear algebra, ensuring mastery over complex mathematical principles and problem-solving techniques.
Deep Conceptual Understanding
Students and professionals will achieve a robust understanding of linear algebra fundamentals and advanced topics, enabling them to tackle complex mathematical problems and enhance their analytical thinking.
Career Enhancement
Prepare for significant career advancements by mastering skills that are in high demand across industries, particularly in technology and engineering (Machine Learning and AI), positioning you as a valuable asset in any workforce like.
Practical Application Skills
Participants will learn to apply linear algebra techniques directly to real-world scenarios in data science, machine learning, and AI, improving their ability to develop algorithms and solve technical challenges efficiently.
Table of Contents
1 Welcome Message
2 Linear Algebra RoadMap 2024
3 Pre-Requisites Introduction
4 Refreshment – Norms & Euclidean Distance
5 Refreshment – Real Numbers and Vector Space
6 Refreshment – Cartesian Coordinate System & Unit Circle
7 Refreshment – Angles, Unit Circle and Trigonometry
8 Refreshment – Pythagorean Theorem & Orthogonality
9 Why these Pre-Requisites Matter
10 Module 2.1: Foundations of Vectors
11 Module 2.2: Special Vectors and Operations
12 Module 2.3: Part 1 – Scalar Multiplication
13 Module 2.3 Part 2 – Linear Combination and Unit Vectors
14 Module 2.3 Part 3 – Span of Vectors
15 Module 2.3: Part 4 – Linear Independence
16 Module 2.4: Dot Product, Cauchy-Schwarz Inequality and Its
17 Module 1: Foundations of Linear Systems and Matrices
18 Module 2: Introduction to Matrices
19 Module 3: Core Matrix Operations
20 Module 4: Part 1 Solving Linear Systems – Gaussian Reduction
21 Module 4: Part 2 Solving Linear Systems – Gaussian Reduction
22 Module 4: Part 3 Solving Linear Systems – Gaussian Reduction
23 Module 4: Part 4 Solving Linear Systems – Gaussian Reduction
24 Module 1: Algebraic Laws for Matrices
25 Module 2: Determinants and Their Properties
26 Module 3: Matrix Inverses and Identity Matrix
27 Module 4: Transpose of Matrices: Properties and Applications
28 Module 1: Part 1 Basis of Vector Space
29 Module 1: Part 2 Vector Projection and Calculation
30 Module 1: Part 3 Gram-Schmidt Process
31 Module 2: Special Matrices and Their Properties
32 Module 3: Matrix Factorization, Examples and Applications
33 Module 4: QR Decomposition Overview
34 Module 5: Eigenvalues, Eigenvectors, and Eigen Decomposition
35 Module 6: Singular Value Decomposition (SVD)
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