Master Linear Algebra: Machine Learning Mastery 2024

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Datagai Academy / Ganeshraj Shetty

7:43:22

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1 - 1 Introduction to Linear Algebra.mp4

10:01

2 - 2 Geometric Representation of an Expression.mp4

09:37

3 - 3 Importance of System of Linear Equation.mp4

15:00

4 - 4 Vector Representation of Linear Equation.mp4

10:26

5 - 5 Introduction to Vectors.mp4

09:54

6 - 6 Vector Magnitude and Direction.mp4

12:36

7 - 7 Application of Magnitude of a Vector.mp4

08:01

8 - 8 Position and Displacement Vector.mp4

05:42

9 - 9 Addition Subtraction and Scalar Operation of a Vector.mp4

07:19

10 - 10 Dot Product between Vectors.mp4

21:24

11 - 11 Projection of a Vector.mp4

06:50

12 - 12 Application of Projection of a Vector.mp4

08:25

13 - 13 Vector Space Subspace.mp4

18:00

14 - 14 Feature Space of a Vector.mp4

07:59

15 - 15 Span of Vectors.mp4

23:26

16 - 16 Linear Independence of Vectors.mp4

06:46

17 - 17 Application of Linearly Independent Vectors.mp4

07:04

18 - 18 Basis and Dimension of a Subspace.mp4

08:49

19 - 19 Gaussian Elimination.mp4

13:13

20 - 20 Gaussian Elimination Application.mp4

12:29

21 - 21 Orthogonal Basis.mp4

10:30

22 - 22 Orthonormal Basis.mp4

03:47

23 - 23 Gram Schmidt Orthogonalization.mp4

07:59

24 - 24 Span Visualization.mp4

07:27

25 - 25 Linear Transformation.mp4

15:56

26 - 26 Kernel and Image.mp4

06:49

27 - 27 Application of Linear Transformation.mp4

10:33

28 - 28 Application of Linear Transformation.mp4

12:00

29 - 29 Types of Matrix and Equations.mp4

09:44

30 - 30 Determinant and its Applications.mp4

18:44

31 - 31 Inverse of a Matrix.mp4

06:23

32 - 32 Determinants II.mp4

10:48

33 - 33 Inverse of a Matrix II.mp4

11:14

34 - 34 Eigen Values and Eigen Vectors.mp4

22:17

35 - 35 Similar Matrix.mp4

11:31

36 - 36 Diagonalization of a Matrix.mp4

25:28

37 - 37 Eigen Decomposition.mp4

16:22

38 - 38 Orthognal Matrix and Properties.mp4

07:13

39 - 39 Symmetric matrix and Properties.mp4

08:00

40 - 40 Singular Value Decomposition.mp4

17:36

Description

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

Who is this for?

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.

What You Need to Know?

Basics of Mathematics and Python Programming

More details

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!!!
Who this course is for:
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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