Mastering Machine Learning with Scikit-Learn and PyTorch (Paperback)

Bryan L. Chang

ISBN 13: 9798266739895

Published by Independently Published, 2025

About this Item

Paperback. Have you ever wanted to truly understand machine learning, not just superficially, but in a way that allows you to build, experiment, and master intelligent systems from scratch? Are you tired of jumping between tutorials, struggling with scattered resources, and never getting a complete picture of how modern machine learning really works?Mastering Machine Learning with Scikit-Learn and PyTorch is designed to be your comprehensive guide. Whether you're a beginner looking to grasp the fundamentals or a professional aiming to sharpen your skills, this book walks you through every step of creating powerful, real-world machine learning systems.Have you wondered how to prepare and transform raw data into insights? Or how to choose the right model, optimize its performance, and validate it for accuracy? This book covers all of that in depth. From classic predictive models to advanced neural networks, you will learn how to design, implement, and troubleshoot algorithms effectively.Do you want to explore deep learning without being overwhelmed by complexity? This book takes you through neural networks, convolutional networks for image processing, recurrent models for sequences, and even the cutting-edge concepts of attention mechanisms. Each concept is explained clearly, with practical, hands-on examples so you can implement them using approachable tools for learning and experimentation.Are you looking for guidance on scaling your models, deploying them, and integrating them into real applications? This book goes beyond theory to show how machine learning can be applied to real-world problems, from data preprocessing to model deployment, and even responsible AI practices for ethical decision-making.By the end of this book, you won't just know machine learning-you will understand it, master it, and be ready to apply it. You'll gain confidence in both structured algorithms and flexible deep learning frameworks, giving you the skills to tackle any data-driven challenge.If you've ever wanted a single, reliable resource that turns confusion into clarity, theory into practice, and data into intelligence, this book is for you. Get ready to transform your understanding of machine learning and start building intelligent systems with confidence. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Seller Inventory # 9798266739895

Bibliographic Details

• Title: Mastering Machine Learning with Scikit-Learn and PyTorch (Paperback)
• Publisher: Independently Published
• Publication Date: 2025
• Language: English
• ISBN 13: 9798266739895
• Binding: Paperback
• Condition: new

About this title

Synopsis

Have you ever wanted to truly understand machine learning, not just superficially, but in a way that allows you to build, experiment, and master intelligent systems from scratch? Are you tired of jumping between tutorials, struggling with scattered resources, and never getting a complete picture of how modern machine learning really works?

Mastering Machine Learning with Scikit-Learn and PyTorch is designed to be your comprehensive guide. Whether you’re a beginner looking to grasp the fundamentals or a professional aiming to sharpen your skills, this book walks you through every step of creating powerful, real-world machine learning systems.

Have you wondered how to prepare and transform raw data into insights? Or how to choose the right model, optimize its performance, and validate it for accuracy? This book covers all of that in depth. From classic predictive models to advanced neural networks, you will learn how to design, implement, and troubleshoot algorithms effectively.

Do you want to explore deep learning without being overwhelmed by complexity? This book takes you through neural networks, convolutional networks for image processing, recurrent models for sequences, and even the cutting-edge concepts of attention mechanisms. Each concept is explained clearly, with practical, hands-on examples so you can implement them using approachable tools for learning and experimentation.

Are you looking for guidance on scaling your models, deploying them, and integrating them into real applications? This book goes beyond theory to show how machine learning can be applied to real-world problems, from data preprocessing to model deployment, and even responsible AI practices for ethical decision-making.

By the end of this book, you won’t just know machine learning—you will understand it, master it, and be ready to apply it. You’ll gain confidence in both structured algorithms and flexible deep learning frameworks, giving you the skills to tackle any data-driven challenge.

If you’ve ever wanted a single, reliable resource that turns confusion into clarity, theory into practice, and data into intelligence, this book is for you. Get ready to transform your understanding of machine learning and start building intelligent systems with confidence.