Linear Algebra Mastery: Elevate Your Machine Learning Skills

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Posted on 01 May 06:17 | by BaDshaH | 4 views

Last updated 4/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.59 GB | 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.

Homepage

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