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🧩 The Object-Oriented Approach to Problem Solving and Machine Learning with Python (2025)

📘 Title: The Object-Oriented Approach to Problem Solving and Machine Learning with Python
👨‍💻 Authors: Sujith Samuel Mathew, Mohammad Amin Kuhail, Maha Hadid, Shahbano Farooq
🏢 Publisher: Independently Published
📅 Year: 2025
📥 👉 DOWNLOAD Free PDF NOW

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🚀 A Unified Approach to Python, Object-Oriented Design, and Machine Learning

Modern machine learning isn’t just about building models — it’s about designing intelligent, reusable, and scalable systems.

“The Object-Oriented Approach to Problem Solving and Machine Learning with Python” (2025) bridges the gap between software engineering principles and machine learning practices, offering a structured approach to solving real-world computational problems.

By combining object-oriented programming (OOP) fundamentals with Python-based ML frameworks, this book teaches how to build modular, maintainable, and production-ready machine learning systems.

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📚 What You’ll Learn Inside

🧱 1. Python and Object-Oriented Foundations

• Classes, objects, and methods in real-world problem contexts
• Inheritance, polymorphism, and encapsulation simplified
• How OOP principles improve code clarity and reusability
• Implementing design patterns to structure AI applications

⚙️ 2. Problem Solving with Object-Oriented Thinking

• Decomposing problems into smaller, reusable components
• Modeling real-world systems using OOP abstractions
• Designing modular Python applications for scalability
• Debugging and refactoring OOP-based projects efficiently

🤖 3. Machine Learning Through the OOP Lens

• Understanding machine learning pipelines as object hierarchies
• Building ML models as modular components (data loaders, preprocessors, trainers, evaluators)
• Integrating scikit-learn, TensorFlow, and PyTorch into object-oriented architectures
• Structuring data workflows and predictive models using reusable Python classes

🧠 4. Hands-On Case Studies

• Implementing object-oriented ML systems for classification and regression tasks
• Designing AI-driven solutions for image, text, and time-series data
• Case studies in finance, education, and healthcare analytics
• End-to-end ML project design with emphasis on maintainability

🔒 5. Software Engineering Meets AI

• Version control, documentation, and unit testing for ML pipelines
• Applying software engineering principles in AI system design
• Best practices for deploying OOP-based ML models in production

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👨‍💻 Who This Book Is For

✅ Python developers looking to apply OOP principles to AI and data science
✅ Machine learning practitioners seeking cleaner, more scalable codebases
✅ Students and researchers learning both OOP and ML in one cohesive guide
✅ Software engineers transitioning into AI-driven applications
✅ Educators who teach Python programming and applied ML together

💡 This book takes you from object-oriented basics to advanced ML system design — all in Python.

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💡 Why This Book Stands Out

• 📘 Combines OOP and Machine Learning in a unified framework
• 🧠 Encourages clean architecture and modular design for AI projects
• 💻 Includes hands-on Python examples and end-to-end case studies
• 🌍 Written by a multi-disciplinary author team with expertise in both computer science and AI
• ⚙️ Ideal for learners transitioning from software development to machine learning

🎯 Learn to think like a developer, design like an engineer, and build like a data scientist — all with Python.

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📌 “The best machine learning systems aren’t just trained — they’re engineered with design in mind.”

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