Mastering Linear Algebra For Machine Learning & Ai 
Published 4/2024 
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz 
Language: English

| Size: 2.59 GB[/align]
| Duration: 7h 43m 
Building Blocks for Machine Intelligence: A Comprehensive Guide to Linear Algebra

[b]What you'll learn[/b]

Develop a solid understanding of fundamental concepts in linear algebra, such as vectors, matrices, and operations on them.

Master techniques for manipulating matrices, including addition, subtraction, scalar multiplication, and matrix multiplication.

Gain proficiency in working with vector spaces, understanding concepts such as linear independence, span, basis, and dimension.

Learn about eigenvalues and eigenvectors and their significance in machine learning algorithms like Singular Value Decomposition.

Comprehend linear transformations and their representations as matrices, and understand how they relate to machine learning tasks like dimensionality reduction.

Acquire skills in solving systems of linear equations using techniques like Gaussian elimination and matrix inversion.

Gain insights into how linear algebra concepts are applied in various machine learning algorithms such as regression and classification.

Understanding how geometric interpretations apply to machine learning tasks such as regression, classification, and dimensionality reduction.

[b]Requirements[/b]

Basics of Mathematics and Python Programming

[b]Description[/b]

In this meticulously crafted Linear Algebra course, you'll delve deep into the fundamental concepts of linear algebra, vectors, matrices, and linear transformations, unraveling their mysteries through a blend of intuitive explanations with examples. Whether you're a novice seeking to embark on your Linear Algebra journey or a seasoned practitioner aiming to deepen your understanding, this course caters to learners of all backgrounds and skill levels.Through engaging lectures, geometric visualizations, and real-world application examples, you'll gain proficiency in manipulating matrices, understanding vector spaces, and deciphering the geometric interpretations underlying key concepts of linear algebra. From eigenvalues and eigenvectors to matrix decompositions, each video equips you with the fundamental knowledge necessary to tackle a myriad of machine learning challenges. With simple hands-on coding exercises using Python and industry-standard libraries like NumPy, you'll translate theoretical concepts into tangible solutions.Whether you aspire to unlock the mysteries of deep learning, revolutionize data analysis, or pioneer groundbreaking AI research, mastering linear algebra is your gateway to the forefront of machine intelligence. Join us on this exhilarating voyage as we embark on a quest to unravel the secrets of intelligence and harness the full potential of linear algebra in the realm of machine learning.Embark on a learning journey packed with 15 hours worth of enriching content neatly compressed into a captivating 7.5-hour video series. Unveil a smarter way to learn, saving you precious time equivalent to another 7.5 hours. Let's dive in and unlock the secrets to efficient learning!We promise that this course will be an asset for you in your journey into the field of Machine Learning and Artificial Intelligence.May Your search for the best course on Linear Algebra end with Us today.Happy Learning!!!

Overview

Section 1: Mastering Linear Algebra for Machine Learning and AI

Lecture 1 Introduction to Linear Algebra for Machine Learning

Lecture 2 Geometric Representation of a Linear Algebraic Expression

Lecture 3 Importance of System Linear Equations

Lecture 4 Vector Representation of Linear Equation

Lecture 5 Introduction to Vectors

Lecture 6 Vector Magnitude and Direction

Lecture 7 Application of Magnitude and Direction

Lecture 8 Position and Displacement Vector

Lecture 9 Addition, Subtraction and Scalar Operation of a Vector

Lecture 10 Dot Product between Vectors

Lecture 11 Projection of a Vector

Lecture 12 Application of Projection of a Vector

Lecture 13 Vector Space & its Subspace

Lecture 14 Feature Space of Vectors

Lecture 15 Span of Vectors

Lecture 16 Linear Independence of Vectors

Lecture 17 Application of Linearly Independent Vectors

Lecture 18 Basis ,Dimension of a Subspace

Lecture 19 Gaussian Elimination Method

Lecture 20 Gaussian Elimination Application

Lecture 21 Orthogonal Basis

Lecture 22 Orthonormal Basis

Lecture 23 Gram Schmidt Orthogonalization Process

Lecture 24 Visualization of Span of Vectors

Lecture 25 Linear Transformation of a Vector

Lecture 26 Kernel and Image of a Transformation

Lecture 27 Application of Linear transformation I

Lecture 28 Application of Linear transformation II

Lecture 29 Types of Matrix and Equations

Lecture 30 Determinant and its Applications

Lecture 31 Inverse of a matrix

Lecture 32 Determinants II

Lecture 33 Inverse of a Matrix II

Lecture 34 Eigen Values and Eigen Vectors of a Matrix

Lecture 35 Similar Matrix and Similarity Transformation

Lecture 36 Diagonalization of a Matrix

Lecture 37 Eigen Decomposition of a Matrix

Lecture 38 Orthogonal Matrix and its Properties

Lecture 39 Symmetric Matrix and its Properties

Lecture 40 Singular Value Decomposition and its Properties

Beginner in Machine Learning and Artificial Intelligence,Undergraduate or graduate students majoring in fields related to computer science, mathematics, engineering, or data science who need a solid foundation in linear algebra for their studies.,Professionals working in industries such as data science, machine learning, artificial intelligence, computer vision, and robotics who want to enhance their skills and knowledge in linear algebra for practical applications.,Educators and instructors who teach courses in linear algebra, machine learning, or related subjects and who may use it as a resource for curriculum development.,Individuals who are self-driven learners interested in acquiring skills in linear algebra for personal or professional development, particularly those aiming to transition into careers in data science, machine learning, or related fields.

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