[ENG] This repository explores the key mathematical concepts behind artificial intelligence and machine learning, providing unified explanations, practical examples, and real-world applications. It's ideal for students, researchers, and practitioners who want to build a strong theoretical and practical foundation for data-driven projects.

1.Machine Learning Fundamentals: Clear explanations of core concepts such as regression, neural networks, convolutional networks, and optimization techniques.

2.Probability & Markov Processes: Understanding randomness, uncertainty, and sequential models with real applications in AI.

3.Differential Equations: Explore how dynamic systems and differential models apply to machine learning and neural dynamics.

4.Mathematical Unification: Presenting machine learning models within a coherent mathematical framework.

5.Graphs and Network Data: Representing and analyzing structured data using graph theory and network science.

6.Dimensionality Reduction & Image Processing: Learn techniques like PCA and t-SNE to visualize and transform real-world, high-dimensional data, including images.

7.Project-Oriented Thinking: Apply mathematical models across a variety of real data projects, with a focus on interpretability and performance.

8.Implications & Limitations of AI: Critical thinking about what AI can and cannot do — ethical, theoretical, and technical boundaries.