Linear Algebra Mastery: Elevate Your Machine Learning Skills

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Linear Algebra Mastery: Elevate Your Machine Learning Skills
Last updated 4/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English

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

What you'll learn

Master the fundamentals of vectors, including vector addition, scalar multiplication, vector norms, and dot products.

Understand vector spaces, subspaces, and linear transformations, crucial for manipulating data in machine learning algorithms.

Master matrix decompositions and eigenvalues/eigenvectors, vital for dimensionality reduction (e.g., PCA) and spectral clustering in ML.

Apply vector operations to manipulate and analyze data representations, such as feature vectors in classification tasks or weight vectors in neural networks

Requirements

Basics of Mathematics and Python Programming

Description

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 and hands-on exercises. 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 module 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.May Your search for the best course on Linear Algebra end with Us.Happy Learning!!!

Overview

Section 1: Introduction

Lecture 1 1. Introduction to Linear Algebra

Lecture 2 2. Geometric Representation of an Expression

Lecture 3 3. Importance of System of Linear Equation

Lecture 4 4. Vector Representation of Linear Equation

Lecture 5 5. Introduction to Vectors

Lecture 6 6. Vector Magnitude and Direction

Lecture 7 7. Application of Magnitude of a Vector

Lecture 8 8. Position and Displacement Vector

Lecture 9 9. Addition Subtraction and Scalar Operation of a Vector

Lecture 10 10. Dot Product between Vectors

Lecture 11 11. Projection of a Vector

Lecture 12 12. Application of Projection of a Vector

Lecture 13 13. Vector Space & Subspace

Lecture 14 14. Feature Space of a Vector

Lecture 15 15. Span of Vectors

Lecture 16 16. Linear Independence of Vectors

Lecture 17 17. Application of Linearly Independent Vectors

Lecture 18 18. Basis and Dimension of a Subspace

Lecture 19 19. Gaussian Elimination

Lecture 20 20. Gaussian Elimination Application

Lecture 21 21. Orthogonal Basis

Lecture 22 22. Orthonormal Basis

Lecture 23 23. Gram Schmidt Orthogonalization

Lecture 24 24. Span Visualization

Lecture 25 25. Linear Transformation

Lecture 26 26. Kernel and Image

Lecture 27 27. Application of Linear Transformation

Lecture 28 28. Application of Linear Transformation

Lecture 29 29. Types of Matrix and Equations

Lecture 30 30. Determinant and its Applications

Lecture 31 31. Inverse of a Matrix

Lecture 32 32. Determinants II

Lecture 33 33. Inverse of a Matrix II

Lecture 34 34. Eigen Values and Eigen Vectors

Lecture 35 35. Similar Matrix

Lecture 36 36. Diagonalization of a Matrix

Lecture 37 37. Eigen Decomposition

Lecture 38 38. Orthognal Matrix and Properties

Lecture 39 39. Symmetric matrix and Properties

Lecture 40 40. Singular Value Decomposition

For Machine Learning, Deep Learning and AI Engineers who wish to gain a strong foundation in understand the working of Machine Learning Algorithms.,For Data Science and Machine Learning Enthusiasts.,For Data Analysts who wish to Make a transition into Data Science and Machine Learning.,For Students who wish to pursue masters in Machine Learning or Deep Learning or Artificial Intelligence.,For Math Graduates who wish to Make a transition into Machine Learning, Deep Learning and Artificial Intelligence Roles.,For every graduate as we are in the Era of Machine Learning and Artificial Intelligence.,For aspiring future Data Scientists.

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