PYTHON for AI, Machine Learning & Intelligent Automation: From Core Concepts to Deep Learning, Smart Systems and Real-World

Few technologies have moved from the research laboratory into everyday life as quickly as artificial intelligence. Within a single decade, systems that learn from data have become part of how we search for information, diagnose disease, manage money, grow food, write software and run factories. At the centre of this transformation sits a surprisingly friendly programming language. Python, created as a readable general-purpose language, has become the common tongue of data scientists, machine learning engineers, automation specialists and researchers across the world.This book was written to give students and professionals a single, coherent path through that landscape. Too often, learners meet Python in one course, statistics in another, machine learning in a third and automation somewhere on the job, without ever seeing how the pieces fit together. Python for AI, Machine Learning & Intelligent Automation brings these threads into one narrative. It begins with the language itself, builds the numerical and data-handling skills that every practitioner needs, develops the mathematics of learning in an intuitive way, and then moves steadily through classical machine learning, deep learning, natural language processing and computer vision. The final part turns this knowledge into action: automating business workflows, controlling physical devices, deploying models as services and building complete projects.Who This Book Is ForThe book is designed for undergraduate and postgraduate students of computer science, engineering, data science and management, as well as working professionals who wish to add AI and automation to their toolkit. No prior experience of machine learning is assumed. Readers who already program in another language will move quickly through the early chapters, while complete beginners will find the Python essentials sufficient to follow everything that comes later. Mathematics is introduced only when it is needed, and always alongside code that makes the ideas concrete.How the Book Is OrganisedThe seventeen chapters are arranged in four parts that mirror the journey of a practitioner.Foundations (Chapters 1–6) introduce AI and intelligent automation, the Python language, NumPy, pandas, visualisation and the mathematics behind learning algorithms.Machine Learning (Chapters 7–10) cover the complete scikit-learn workflow, regression, classification, clustering, dimensionality reduction, model evaluation and tuning.Deep Learning and Modern AI (Chapters 11–13) build neural networks from first principles and then with PyTorch and Keras, before exploring computer vision, natural language processing, transformers and large language models.Intelligent Automation and Deployment (Chapters 14–17) apply these skills to workflow automation, web scraping, AI agents, IoT with the Raspberry Pi, model deployment and MLOps, ending with capstone projects that integrate the entire book.Features of the BookEvery chapter opens with clear learning objectives and closes with a summary, review questions and programming exercises. Throughout the text you will find more than one hundred complete code listings, many of them shown together with the actual output they produce, together with carefully drawn figures, comparison tables, circuit diagrams for hardware projects, and highlighted notes, tips and cautions that capture the practical wisdom that usually takes years to acquire. Worked examples and mini-projects appear in every chapter so that each idea is immediately put to use.
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