Before Machine Learning Volume 3 - Probability and Statistics for A.I : Master Probability, Statistics, and Their Role in AI's Future Evolution / Jorge BRASIL / PACKT PUBLISHING (2025)  

Titre : Before Machine Learning Volume 3 - Probability and Statistics for A.I : Master Probability, Statistics, and Their Role in AI's Future Evolution Type de document : e-book Auteurs : Jorge BRASIL Editeur : PACKT PUBLISHING Année de publication : 2025 ISBN/ISSN/EAN : 9781837028597 Note générale : copyrighted Langues : Anglais (eng) Résumé : Explore the critical role of probability and statistics in building AI systems. A detailed resource for machine learning enthusiasts to solidify their understanding of the mathematical and statistical underpinnings of AI.Key FeaturesDetailed exploration of probability and statistics in AI developmentStep-by-step explanation of key statistical concepts with practical applicationsA comprehensive coverage of models, Markov processes, and hierarchical techniquesBook DescriptionDelve into the importance of probability and statistics in AI, beginning with fundamental measures like mean, median, and variance. This book takes you on a journey through the basics of probability theory, introducing key concepts such as central tendency, variance, and probability distributions. It emphasizes the role of statistical measures in understanding and analyzing data. Building on these foundations, the book explores hypothesis testing, Bayesian inference, and statistical distributions in-depth. Readers will gain practical insights into essential techniques for model evaluation, maximum likelihood estimation, and the interpretation of data in the context of AI applications. Each concept is illustrated with practical examples and case studies to ensure clarity and application. Finally, advanced topics like Markov processes, hierarchical Bayesian models, and multivariate distributions are introduced. The book addresses critical areas like variance, correlation, and hypothesis testing, equipping readers with the skills to tackle real-world challenges in AI and machine learning. Whether you're a student, professional, or AI enthusiast, this book offers the essential statistical tools and knowledge to excel in the field.What you will learnUnderstand probability theory and its foundational role in AIExplore statistical measures and distributions for data analysisApply Bayesian models for decision-making processesLearn hypothesis testing and model evaluation techniquesMaster Markov models for sequential data analysisUnderstand hierarchical Bayesian models and their applicationsWho this book is forStudents and professionals in data science, artificial intelligence, and machine learning will find this book invaluable. A solid understanding of high school-level algebra and basic calculus is required. This book is ideal for readers who aim to strengthen their statistical and probabilistic skills for use in artificial intelligence applications. It is also beneficial for academics and researchers who want a comprehensive resource on probability and statistics in machine learning. Nombre d'accès : Illimité En ligne : https://neoma-bs.idm.oclc.org/login?url=https://www.scholarvox.com/book/88967007 Permalink : https://cataloguelibrary.neoma-bs.fr/index.php?lvl=notice_display&id=601363

Before Machine Learning Volume 1 - Linear Algebra for A.I : The Fundamental Mathematics for Data Science and Artificial Intelligence / Jorge BRASIL / PACKT PUBLISHING (2024)  

Titre : Before Machine Learning Volume 1 - Linear Algebra for A.I : The Fundamental Mathematics for Data Science and Artificial Intelligence Type de document : e-book Auteurs : Jorge BRASIL Editeur : PACKT PUBLISHING Année de publication : 2024 ISBN/ISSN/EAN : 9781836208952 Note générale : copyrighted Langues : Anglais (eng) Résumé : Unlock the essentials of linear algebra to build a strong foundation for machine learning. Dive into vectors, matrices, and principal component analysis with expert guidance in "Before Machine Learning Volume 1 - Linear Algebra."Key FeaturesComprehensive introduction to linear algebra for machine learningDetailed exploration of vectors and matricesIn-depth study of principal component analysis (PCA)Book DescriptionIn this book, you'll embark on a comprehensive journey through the fundamentals of linear algebra, a critical component for any aspiring machine learning expert. Starting with an introductory overview, the course explains why linear algebra is indispensable for machine learning, setting the stage for deeper exploration. You'll then dive into the concepts of vectors and matrices, understanding their definitions, properties, and practical applications in the field. As you progress, the course takes a closer look at matrix decomposition, breaking down complex matrices into simpler, more manageable forms. This section emphasizes the importance of decomposition techniques in simplifying computations and enhancing data analysis. The final chapter focuses on principal component analysis, a powerful technique for dimensionality reduction that is widely used in machine learning and data science. By the end of the course, you will have a solid grasp of how PCA can be applied to streamline data and improve model performance. This course is designed to provide technical professionals with a thorough understanding of linear algebra's role in machine learning. By the end, you'll be well-equipped with the knowledge and skills needed to apply linear algebra in practical machine learning scenarios.What you will learnUnderstand the fundamental concepts of vectors and matricesImplement principal component analysis in data reductionAnalyze the role of linear algebra in machine learningEnhance problem-solving skills through practical applicationsGain the ability to interpret and manipulate high-dimensional dataBuild confidence in using linear algebra for data science projectsWho this book is forThis course is ideal for technical professionals, data scientists, aspiring machine learning engineers, and students of computer science or related fields. Additionally, it is beneficial for software developers, engineers, and IT professionals seeking to transition into data science or machine learning roles. A basic understanding of high school-level mathematics is recommended but not required, making it accessible for those looking to build a foundational understanding before diving into more advanced topics. Nombre d'accès : Illimité En ligne : https://neoma-bs.idm.oclc.org/login?url=https://www.scholarvox.com/book/88957617 Permalink : https://cataloguelibrary.neoma-bs.fr/index.php?lvl=notice_display&id=585540

Before Machine Learning Volume 2 - Calculus for A.I : The Fundamental Mathematics for Data Science and Artificial Intelligence / Jorge BRASIL / PACKT PUBLISHING (2024)  

Titre : Before Machine Learning Volume 2 - Calculus for A.I : The Fundamental Mathematics for Data Science and Artificial Intelligence Type de document : e-book Auteurs : Jorge BRASIL Editeur : PACKT PUBLISHING Année de publication : 2024 ISBN/ISSN/EAN : 9781836200697 Note générale : copyrighted Langues : Anglais (eng) Résumé : Deepen your calculus foundation for AI and machine learning with essential concepts like derivatives, integrals, and multivariable calculus, all applied directly to neural networks and optimization.Key FeaturesA step-by-step guide to calculus concepts tailored for AI and machine learning applicationsClear explanations of advanced topics like Taylor Series, gradient descent, and backpropagationPractical insights connecting calculus principles directly to neural networks and data scienceBook DescriptionThis book takes readers on a structured journey through calculus fundamentals essential for AI. Starting with “Why Calculus?” it introduces key concepts like functions, limits, and derivatives, providing a solid foundation for understanding machine learning. As readers progress, they will encounter practical applications such as Taylor Series for curve fitting, gradient descent for optimization, and L'Hôpital’s Rule for managing undefined expressions. Each chapter builds up from core calculus to multidimensional topics, making complex ideas accessible and applicable to AI. The final chapters guide readers through multivariable calculus, including advanced concepts like the gradient, Hessian, and backpropagation, crucial for neural networks. From optimizing models to understanding cost functions, this book equips readers with the calculus skills needed to confidently tackle AI challenges, offering insights that make complex calculus both manageable and deeply relevant to machine learning.What you will learnExplore the essentials of calculus for machine learningCalculate derivatives and apply them in optimization tasksAnalyze functions, limits, and continuity in data scienceApply Taylor Series for predictive curve modelingUse gradient descent for effective cost-minimizationImplement multivariable calculus in neural networksWho this book is for Aspiring AI engineers, machine learning students, and data scientists will find this book valuable for building a strong calculus foundation. A basic understanding of calculus is beneficial, but the book introduces essential concepts gradually for all levels. Nombre d'accès : Illimité En ligne : https://neoma-bs.idm.oclc.org/login?url=https://www.scholarvox.com/book/88961481 Permalink : https://cataloguelibrary.neoma-bs.fr/index.php?lvl=notice_display&id=594975