Machine Learning with Python: From Scikit-Learn Fundamentals to Real-World Predictive Models
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كتاب Machine Learning With Python From Scikit Learn Fundamentals To Real World Predictive Models
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غير متوفر رقمياً أو ورقياً من خلال مكتبة نور، متروك للتقييم والمراجعة
Machine Learning With Python: From Scikit-Learn Fundamentals To Real-World Predictive Models
| مؤلف: | Mohammad Belal Ansari |
| قسم: | الذكاء الصناعي وتعليم الألة [تعديل] |
| اللغة: | الإنجليزية |
| ترتيب الشهرة: | 1,078,879 رقم 1 هو الأشهر ! |
| رابط مختصر: | نسخ |
| المزيد من الكتب مثل هذا الكتاب | |
Mohammad Belal Ansari
Author, Researcher and Entrepreneur
الناشر والمؤلف
كتاب Machine Learning with Python: From Scikit-Learn Fundamentals to Real-World Predictive Models .
Mohammad Belal Ansari is an independent researcher, science writer, and entrepreneur with a strong interest in physics, cosmology, climate technology, and emerging scientific ideas. He holds a Master's degree in Physics and is passionate about making complex scientific concepts accessible to a wider audience.
His work explores some of the most profound questions in modern science, including the nature of time, relativity, quantum mechanics, black holes, consciousness, and the future of the universe. Through his writing, he aims to bridge the gap between advanced scientific research and curious readers seeking to understand the mysteries of reality.
In addition to his work as an author, Mohammad Belal Ansari is the founder of Ecoso Next Generation Pvt. Ltd., a climate-tech venture focused on innovative approaches to carbon capture and sustainable technologies.
He believes that science is not merely a collection of facts but a journey of discovery that expands our understanding of the universe and our place within it.
When he is not writing or researching, he explores new ideas at the intersection of physics, technology, environment, and innovation.
وصف الكتاب
Master the foundations of modern machine learning with Machine Learning with Python: From Scikit-Learn Fundamentals to Real-World Predictive Models—a practical, hands-on guide designed for students, aspiring data scientists, developers, researchers, and professionals.
This book takes you step by step through the complete machine learning workflow, from understanding core concepts and preparing data to building, evaluating, and deploying predictive models using Python and Scikit-Learn.
Through clear explanations, practical examples, and real-world projects, you will learn how to transform raw data into actionable insights and develop intelligent systems that solve real business problems.
Inside this book, you will learn how to:
• Understand the fundamentals of machine learning, including supervised and unsupervised learning.
• Work with essential Python libraries such as NumPy, Pandas, Matplotlib, and Scikit-Learn.
• Prepare, clean, and visualize data for effective model development.
• Build classification, regression, clustering, and dimensionality reduction models.
• Apply feature engineering, model selection, and hyperparameter tuning techniques.
• Evaluate model performance using industry-standard metrics and validation strategies.
• Develop real-world projects, including customer churn prediction, house price estimation, and sales forecasting.
• Deploy machine learning models and monitor their performance in production environments.
• Implement best practices for responsible AI, model governance, and ethical decision-making.
Whether you are new to machine learning or looking to strengthen your practical skills, this book provides the knowledge and tools needed to build reliable, scalable, and data-driven solutions.
By the end of this book, you will be able to confidently design machine learning workflows, create predictive models, and apply machine learning techniques to real-world challenges.
Learn. Build. Predict.
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