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Free Certificate

Linear Algebra for Analysis

Master the essentials of linear algebra in this free course! Learn about vectors, matrices, transformations, eigenvalues, and eigenvectors to enhance your analytics skills and problem-solving capabilities.

5 hours of learning

Data Manipulation

Vectors

Problem Solving

For enquiries call:
18002102020
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Key Highlights Of This Linear Algebra Free Course

What You Will Learn

Linear Transformation & Matrices

 This session will explore matrices and their role in linear transformations, laying the groundwork for more advanced topics in data science and machine learning.

Topics Covered

  • Importance of Linear Transformation
    Linear transformations map vectors from one vector space to another. This section emphasizes how these transformations are represented using matrices. You will understand how linear transformations preserve the structure of vector spaces, which is crucial for solving equations and simplifying complex systems, especially in data transformations.

  • Matrices
    Matrices are arrays of numbers arranged in rows and columns. They serve as a tool to represent linear transformations, and this subtopic will teach you how to work with matrices. You'll cover matrix multiplication, addition, and how matrices are used in various fields such as computer graphics, statistics, and optimization.

  • Matrix Operations
    Learn the core matrix operations: addition, multiplication, and inversion. These operations are essential for solving linear equations, performing data transformations, and computing systems in fields like machine learning and computer vision. You'll understand the importance of matrix operations in manipulating data for various applications.

  • Determinants
    Determinants provide useful information about matrices, such as whether a matrix has an inverse and its geometric interpretation. You'll learn how to calculate and interpret determinants to assess the solvability of linear systems and their implications in solving real-world problems.

  • Linear Independence
    This subtopic explains linear independence, a key concept in determining whether a set of vectors forms a basis for a vector space. It also explores how linear independence relates to the solvability of systems of linear equations and how it is used to simplify and analyze data.

Eigenvalues & Eigenvectors

This session will introduce eigenvectors and eigenvalues, exploring how they are calculated and their relevance to data analysis techniques like Principal Component Analysis (PCA).

Topics Covered

  • Eigenvectors: A Special Type of Vector
    Eigenvectors are vectors that remain unchanged in direction when a linear transformation is applied, though their magnitude may change. This subtopic introduces the concept of eigenvectors and how they help in simplifying complex problems in data analysis, computer science, and physics by reducing the dimensions of data.

  • Calculation of Eigenvalues
    Eigenvalues are scalar values that indicate how much an eigenvector is stretched or shrunk during a linear transformation. You'll learn how to compute eigenvalues using the determinant of a matrix and understand their significance in identifying system behaviors, such as stability in dynamic systems.

  • Applications
    Eigenvalues and eigenvectors play a crucial role in techniques like Principal Component Analysis (PCA), which is widely used in data science for dimensionality reduction. This section explores how these concepts are used in real-world applications, such as image compression, machine learning, and network analysis.

What Are the Benefits of This Course?

This Linear Algebra for Analysis free course is designed to build a strong foundation in linear algebra concepts essential for analytics, data science, and engineering applications. Here's what you gain by enrolling:

100% Free Course – Get unrestricted access to high-quality, expert-designed content on linear algebra, with no fees or hidden charges.

Beginner-Friendly Content with Industry Relevance – Tailored for learners with little to no background in mathematics, this course introduces core linear algebra concepts used in analytical fields like data science, AI, and engineering.

Self-Paced Learning – Learn on your own schedule. This flexible format allows students, professionals, and enthusiasts to progress through the course material at a comfortable pace.

Lifetime Access to Course Material – Revisit and review course content anytime. Whether for revision or further exploration, lifetime access ensures you stay updated and informed.

Certificate of Completion – Earn a recognized certificate after completing the course. Use it to showcase your linear algebra proficiency on your resume, LinkedIn, or professional portfolio.

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Indian Nationals

1800 210 2020

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Foreign Nationals

+918068792934