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Foundational Mathematics for Data Science | Arabic
Foundational Essentials: Linear Algebra, Probability, and Statistics for Data Science
This Course Includes
udemy
4.9 (43 reviews )
6 total hours 
arabic
Online - Self Paced
course
Udemy
About Foundational Mathematics for Data Science | Arabic
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:
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)
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!
What You Will Learn?
- Linear Algebra for Machine Learning.
- Operations on Vectors & Matrices.
- Linear Transformation in Linear Algebra.
- Eigen Values & Eigen Vectors.
- Probability for Data Science & Machine Learning.
- Statistics for Data Science & Machine Learning.
- Different Methods to deal with each type of variables.
- How to deal and analyze with Numerical Variables.
- How to deal and analyze with Categorical Variables.