{"product_id":"artificial-intelligence-and-machine-learning-engineering-with-python-mathematical-foundations-deep-learning-neural-networks-transformers-mlops-a-9798180398024","title":"Artificial Intelligence and Machine Learning Engineering with Python: Mathematical Foundations, Deep Learning, Neural Networks, Transformers, MLOps, a","description":"\u003cp\u003e • Author(s): Ajai Kumar Medhavi\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Artificial Intelligence - Generative AI\u003c\/p\u003e\u003cp\u003ePractical Artificial Intelligence and Machine Learning Engineering\u003cbr\u003eMathematical Foundations, Intelligent Algorithms, Deep Learning Architectures, and Industrial AI Systems using Python\u003c\/p\u003e\u003cp\u003eArtificial Intelligence and Machine Learning are transforming every industry-from manufacturing, healthcare, finance, cybersecurity, and telecommunications to autonomous systems, robotics, and intelligent enterprise applications. Yet many resources focus either on theory without implementation or coding without a solid mathematical foundation.\u003c\/p\u003e\u003cp\u003eThis book bridges that gap.\u003c\/p\u003e\u003cp\u003e\u003cb\u003ePractical Artificial Intelligence and Machine Learning Engineering\u003c\/b\u003e is a comprehensive, industry-focused guide that takes you from core mathematical concepts to the design, development, deployment, and optimization of real-world AI systems using Python. Written for students, software engineers, data scientists, researchers, and technology professionals, this book combines rigorous theory with practical implementation techniques used in modern AI engineering environments.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eInside this book, you will learn: \u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eMathematical foundations for AI and machine learning, including linear algebra, probability, statistics, optimization, and information theory\u003c\/p\u003e\u003cp\u003eSupervised, unsupervised, semi-supervised, and reinforcement learning techniques\u003c\/p\u003e\u003cp\u003eRegression, classification, clustering, dimensionality reduction, and ensemble learning algorithms\u003c\/p\u003e\u003cp\u003eDeep learning architectures, including CNNs, RNNs, LSTMs, GRUs, Autoencoders, GANs, Transformers, and attention mechanisms\u003c\/p\u003e\u003cp\u003eNatural Language Processing (NLP), Large Language Models (LLMs), computer vision, and intelligent perception systems\u003c\/p\u003e\u003cp\u003eFeature engineering, model evaluation, hyperparameter tuning, and performance optimization \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003eScalable AI pipelines, MLOps practices, model deployment, monitoring, and lifecycle management\u003cp\u003eIndustrial AI system design for production-ready environments\u003c\/p\u003e\u003cp\u003eEthical AI, explainable AI, responsible machine learning, and governance frameworks\u003c\/p\u003e\u003cp\u003eEnd-to-end Python implementations using modern AI and machine learning libraries\u003c\/p\u003e\u003cp\u003eUnlike introductory AI books that stop at basic algorithms, this text emphasizes engineering principles required to build reliable, scalable, maintainable, and deployable AI solutions. Readers gain both conceptual understanding and practical skills needed for modern industrial applications.\u003c\/p\u003eWhat Makes This Book Different?\u003cul\u003e\n\u003cli\u003e\u003cp\u003eStrong mathematical rigor without unnecessary complexity\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003ePractical Python-based implementations and engineering workflows\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eCoverage from fundamentals to advanced deep learning systems\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eReal-world AI architecture and deployment considerations\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eIndustry-oriented approach suitable for professional development\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eExtensive explanations, examples, and implementation strategies\u003c\/p\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eWhether you are preparing for a career in Artificial Intelligence, Machine Learning Engineering, Data Science, Deep Learning, Intelligent Automation, or advanced software development, this book provides the knowledge and practical expertise required to design and build modern AI systems with confidence.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eMaster the mathematics. Understand the algorithms. Build intelligent systems. Engineer production-ready AI solutions.\u003c\/b\u003e\u003c\/p\u003e","brand":"Independently Published","offers":[{"title":"Paperback","offer_id":48215047667863,"sku":"9798180398024","price":3918.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9798180398024.webp?v=1788731898","url":"https:\/\/atlanticbooks.com\/products\/artificial-intelligence-and-machine-learning-engineering-with-python-mathematical-foundations-deep-learning-neural-networks-transformers-mlops-a-9798180398024","provider":"Atlantic Books","version":"1.0","type":"link"}