This GitHub repository, "ML_Doc", is a comprehensive guide for learning machine learning algorithms, primarily aimed at Persian-speaking learners. The project aims to provide a step-by-step guide, detailed explanations, practical examples, and sample code in Python for various machine learning algorithms.
- Python
- Jupyter Notebook
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Comprehensive and Step-by-Step Learning: This book includes detailed and simplified explanations of complex concepts in machine learning and deep learning. We strive to use clear and understandable language to cater to individuals at all knowledge levels.
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Coverage of Diverse Algorithms: We delve into the analysis and exploration of various machine learning, deep learning, and reinforcement learning algorithms. These algorithms include classic methods such as decision trees and regression, as well as deep neural networks and reinforcement learning algorithms.
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Practical Projects: To enhance understanding, practical projects are provided in each chapter. These projects allow you to implement the algorithms using real datasets, gaining hands-on experience. This experience will deepen your understanding of the concepts and strengthen your skills in solving real-world problems.
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Resources and References: At the end of each chapter, useful resources and references for further study and deepening your knowledge are provided.
This book is designed not only as an educational resource but also as a practical guide for researchers, students, and enthusiasts in machine learning and deep learning. We hope this book assists you in your learning journey and guides you through the fascinating world of data and artificial intelligence.
The project is open-source and available under the MIT License.