Foundational Mathematics for Data Science | Arabic

Description

This course "Foundational Mathematics for Data Science" provides a comprehensive understanding of linear algebra, statistics, and probability essential for those delving into the realms of machine learning and data science. It stands out as a unique resource in Arabic, offering interactive, application-based training with thorough explanations, catering to beginners' levels to achieve an excellent grasp of the subjects.

Suitable for novices without prerequisites, this course caters to individuals interested in AI, its associated mathematics, data analysts, data scientists, and AI engineers.

What you will learn:

1. Linear Algebra:

   • Introduction and Importance

   • System of Linear Equations

   • Vectors and Operations

   • Vector Norm

   • Dot Product

   • Matrices and Matrix Operations

   • Vector Spaces

   • Linear Combinations

   • Vector Spans

   • Vectors Linear Independence

   • Basis of Space

   • Linear Transformation

   • Determinant

   • System of Linear Equation Solutions

   • Gauss-Jordan Elimination

   • Inverse

   • Eigenvalues and Eigenvectors

   • PCA (Principal Component Analysis)

2. Probability & Statistics:

   • Importance and Relevance

   • Probability vs. Statistics

   • Empirical and Theoretical Probability

   • Joint, Marginal, and Conditional Probability Distribution

   • Random Experiment

   • Random Variables

   • Statistics

   • Sampling Methods

   • Numerical Variables and Visualization Techniques

   • Statistical Tools

   • Categorical Variables and Visualization Techniques

   • Probability Distributions

Whether you're an AI enthusiast, developer, student, or data scientist, this course will empower you with the foundational knowledge of mathematics needed for data science.

Join us now and embark on an enriching learning journey that will set you on the path in the AI field.

Enroll today!

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