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Classification Handbook for Beginners
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Artificial intelligence and machine learning have become indispensable pillars of today's rapidly evolving technological landscape. At the heart of these advancements lie classification algorithms, key tools in the data science domain. From diagnosing diseases to analyzing customer behavior, and from detecting spam emails to recognizing images, these algorithms drive innovation across a broad spectrum of fields.
This book aims to bridge the gap between theory and practice, offering a comprehensive guide to machine learning and classification algorithms. With a balanced approach, it delves into the mathematical foundations of each algorithm while demonstrating their real-world applications using Python. Readers will not only grasp the theoretical underpinnings but also learn how to apply these concepts practically, fostering a deeper understanding and the ability to solve complex problems.
Whether you are a newcomer to data science, an experienced programmer, an academic, or an industry professional, this book is tailored to cater to your needs. Packed with step-by-step explanations and practical Python code examples, it equips readers with the tools to confidently implement machine learning algorithms in their own projects. This collaborative work of five expert authors combines academic rigor with industrial expertise, making it an invaluable resource for anyone embarking on their machine learning journey.
This book is included in DOAB.
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Keywords
- artificial intelligence
- Classification
- machine learning methods
- UYQ
Links
DOI: 10.59537/saupress.2347Editions