Practical Machine Learning And Image Processing
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Practical Machine Learning and Image Processing
Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms discussed in the book. You will then cover all the core image processing algorithms in detail before moving onto the biggest computer vision library: OpenCV. You’ll see the OpenCV algorithms and how to use them for image processing. The next section looks at advanced machine learning and deep learning methods for image processing and classification. You’ll work with concepts such as pulse coupled neural networks, AdaBoost, XG boost, and convolutional neural networks for image-specific applications. Later you’ll explore how models are made in real time and then deployed using various DevOps tools. All the conceptsin Practical Machine Learning and Image Processing are explained using real-life scenarios. After reading this book you will be able to apply image processing techniques and make machine learning models for customized application. What You Will Learn Discover image-processing algorithms and their applications using Python Explore image processing using the OpenCV library Use TensorFlow, scikit-learn, NumPy, and other libraries Work with machine learning and deep learning algorithms for image processing Apply image-processing techniques to five real-time projects Who This Book Is For Data scientists and software developers interested in image processing and computer vision.
Practical Machine Learning: From Pictures to the Cloud 2025
Author: AUTHOR:1-Praneet Amul Akash Cherukuri AUTHOR:2-Dr. Santosh Kumar Henge
language: en
Publisher: RAVEENA PRAKASHAN OPC PVT LTD
Release Date:
PREFACE The past decade has moved machine learning from academic curiosity to an invisible utility pulsing through every photograph we snap and every swipe we make. A face unlocks a phone, a drone inspects a bridge, a doctor consults an algorithm before a diagnosis—all powered by models that see, learn, and act in real time. Yet for students and engineers stepping into the field, the journey from inquisitive “Hello-world” notebook to a production-grade model running on an edge device or a cloud endpoint can feel disjointed and opaque. Practical Machine Learning: From Pictures to the Cloud was born of that gap. In our classrooms and industry collaborations at S R University, we watched learners master isolated concepts—convolutional layers, hyper-parameter tuning, REST APIs—without a blueprint that tied them together. This book offers that blueprint. We start with raw pixels, guide you through feature engineering and modern deep-learning architectures, and then scale the conversation outward: how to train responsibly, deploy at cloud scale, monitor for drift, and govern for fairness and privacy. What makes the text “practical” is its bias toward end-to-end reproducibility. Every chapter couple’s theory with hands-on labs drawn from real engagements in health care, smart cities, retail, and autonomous systems. Code examples ship as containerised notebooks; pipeline diagrams map directly to the managed services of AWS, Google Cloud, Azure, and open-source stacks like Kubeflow and Feast. Whether your workstation is a laptop or a GPU cluster, you can follow the same lifecycle we use in production. Equally vital is the ethical lens threaded throughout. As image models grow more capable, they also magnify risks—bias, surveillance, ecological cost. You will find checklists, case studies, and policy references alongside optimisation tricks, because robustness and responsibility are no longer optional extras; they are success criteria. The book is organised in three movements: 1. Seeing – fundamentals of image data, classical vision, and modern convolutional/transformer networks. 2. Learning – advanced training techniques, transfer learning, hyper-parameter tuning, and explainability. 3. Serving – scalable pipelines, cloud deployment, edge inference, monitoring, cost governance, and compliance. Our intended audience spans senior undergraduates, graduate students, and practitioners who know basic Python and linear algebra but want to take the leap into full-stack machine-learning engineering. We owe gratitude to our students, whose incisive questions shaped the narrative, and to industry partners who opened their architectures for case studies. Any errors that remain are ours alone. We hope this book becomes your desk companion as you turn pixels into insights and models into value—responsibly, reproducibly, and at scale. Authors
Practical Deep Learning
Practical Deep Learning teaches total beginners how to build the datasets and models needed to train neural networks for your own DL projects. If you’ve been curious about artificial intelligence and machine learning but didn’t know where to start, this is the book you’ve been waiting for. Focusing on the subfield of machine learning known as deep learning, it explains core concepts and gives you the foundation you need to start building your own models. Rather than simply outlining recipes for using existing toolkits, Practical Deep Learning teaches you the why of deep learning and will inspire you to explore further. All you need is basic familiarity with computer programming and high school math—the book will cover the rest. After an introduction to Python, you’ll move through key topics like how to build a good training dataset, work with the scikit-learn and Keras libraries, and evaluate your models’ performance. You’ll also learn: How to use classic machine learning models like k-Nearest Neighbors, Random Forests, and Support Vector Machines How neural networks work and how they’re trained How to use convolutional neural networks How to develop a successful deep learning model from scratch You’ll conduct experiments along the way, building to a final case study that incorporates everything you’ve learned. The perfect introduction to this dynamic, ever-expanding field, Practical Deep Learning will give you the skills and confidence to dive into your own machine learning projects.