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Project-Driven Machine Learning with Python: Build, Deploy & Scale Real ML Systems
Paperback
DatabasesGeneral ComputersProgramming
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Publisher: Independently Published
Published: Jan 14 2026
Pages: 276
Weight: 1.06
Height: 0.58 Width: 7.00 Depth: 10.00
Language: English
Project-Driven Machine Learning with Python: Build, Deploy & Scale Real ML Systems
Step into the world of practical machine learning with Project-Driven Machine Learning with Python, a hands-on guide by Python expert A.P. Pyre. This book is designed for developers, data enthusiasts, and professionals who want to learn by building real, deployable ML systems-not just theory.
From data preprocessing and feature engineering to model training, evaluation, optimization, and deployment, every chapter is packed with fully functional Python code, actionable tips, and industry best practices. You'll master cutting-edge tools like scikit-learn, FastAPI, Docker, and learn how to scale models efficiently while maintaining performance.
Through practical projects such as forecasting systems, recommendation engines, and predictive models, you'll gain the skills to turn ideas into deployable applications quickly, saving time while building a strong, professional portfolio. Each project is carefully explained, ensuring you understand not just how, but why each step matters-making this book ideal for both beginners and intermediate learners.
This book combines expert guidance, credibility, and real-world experience to give you an edge in the fast-paced AI landscape. Whether you're up-skilling for your career, building a portfolio, or exploring the latest ML technologies, this book delivers practical knowledge, modern techniques, and actionable strategies in one concise, project-driven format.
Key Features & Benefits:
- Learn Python-based machine learning from scratch through real projects.
- Master data preprocessing, feature engineering, and model optimization.
- Build deployable systems with FastAPI and Docker for production-ready ML solutions.
- Understand model versioning, monitoring, and scaling for real-world applications.
- Develop a professional portfolio to boost career opportunities in AI and data science.
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