Fundamentals Of Machine Learning Artificial Intelligence
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Artificial Intelligence And Machine Learning Fundamentals
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Author : Zsolt Nagy
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-12-12
Artificial Intelligence And Machine Learning Fundamentals written by Zsolt Nagy and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-12 with Computers categories.
Create AI applications in Python and lay the foundations for your career in data science Key FeaturesPractical examples that explain key machine learning algorithmsExplore neural networks in detail with interesting examplesMaster core AI concepts with engaging activitiesBook Description Machine learning and neural networks are pillars on which you can build intelligent applications. Artificial Intelligence and Machine Learning Fundamentals begins by introducing you to Python and discussing AI search algorithms. You will cover in-depth mathematical topics, such as regression and classification, illustrated by Python examples. As you make your way through the book, you will progress to advanced AI techniques and concepts, and work on real-life datasets to form decision trees and clusters. You will be introduced to neural networks, a powerful tool based on Moore's law. By the end of this book, you will be confident when it comes to building your own AI applications with your newly acquired skills! What you will learnUnderstand the importance, principles, and fields of AIImplement basic artificial intelligence concepts with PythonApply regression and classification concepts to real-world problemsPerform predictive analysis using decision trees and random forestsCarry out clustering using the k-means and mean shift algorithmsUnderstand the fundamentals of deep learning via practical examplesWho this book is for Artificial Intelligence and Machine Learning Fundamentals is for software developers and data scientists who want to enrich their projects with machine learning. You do not need any prior experience in AI. However, it’s recommended that you have knowledge of high school-level mathematics and at least one programming language (preferably Python).
Artificial Intelligence And Machine Learning Fundamentals
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Author : Zsolt Nagy
language : en
Publisher:
Release Date : 2019
Artificial Intelligence And Machine Learning Fundamentals written by Zsolt Nagy and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
"Machine learning and neural networks are fast becoming pillars on which you can build intelligent applications. The course will begin by introducing you to Python and discussing using AI search algorithms. You will learn math-heavy topics, such as regression and classification, illustrated by Python examples. You will then progress on to advanced AI techniques and concepts, and work on real-life data sets to form decision trees and clusters. You will be introduced to neural networks, which is a powerful tool benefiting from Moore's law applied on 21st-century computing power. By the end of this course, you will feel confident and look forward to building your own AI applications with your newly-acquired skills!"--Resource description page.
Artificial Intelligence And Machine Learning Principles And Applications
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Author : Dr. Shashi Tanwar
language : en
Publisher: Academic Guru Publishing House
Release Date : 2024-08-07
Artificial Intelligence And Machine Learning Principles And Applications written by Dr. Shashi Tanwar and has been published by Academic Guru Publishing House this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-07 with Study Aids categories.
“Artificial Intelligence and Machine Learning – Principles and Applications” is a comprehensive guide that delves into the core concepts, methodologies, and practical implementations of AI and machine learning. Authored with clarity and expertise, it serves as an indispensable resource for both beginners and seasoned professionals in the field. The book begins by elucidating the fundamental principles underlying artificial intelligence and machine learning, providing readers with a solid foundation to build upon. From there, it progresses into more advanced topics, covering a wide range of algorithms, techniques, and applications across various domains. Readers are guided through the intricacies of machine learning algorithms, including supervised and unsupervised learning, reinforcement learning, and deep learning. Each concept is accompanied by illustrative examples and offers a hands-on approach to learning. Furthermore, the book explores the ethical and societal implications of AI and machine learning, prompting readers to consider the broader implications of their work. It discusses issues such as bias, fairness, privacy, and transparency, encouraging a responsible approach to AI development and deployment. One of the standout features of “Artificial Intelligence and Machine Learning – Principles and Applications” is its emphasis on practical applications. It provides insights into how AI and machine learning techniques can be leveraged to solve complex problems in areas such as healthcare, finance, marketing, and beyond. Overall, this book serves as an invaluable resource for anyone looking to gain a comprehensive understanding of artificial intelligence and machine learning, offering both theoretical insights and practical guidance for real-world implementation.
Machine Learning For Beginners
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Author : Aldrich Hill
language : en
Publisher:
Release Date : 2021-12-10
Machine Learning For Beginners written by Aldrich Hill and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-10 with categories.
Do you need a better knowledge of the possibilities existing in the artificial intelligence available today? Do you want to know how big data will shape the future? Do you want to achieve a professional understanding of the most commonly used machine learning models? Machine learning is a branch of artificial intelligence and computer science becoming increasingly relevant in our modern world. It's a relatively new and progressive way of allowing a computer model to improve over time as it is introduced to more data. With the widespread availability of computers today, most machine learning techniques can be done at home. From the GPS on our phones to the future of self-driving cars, machine learning is becoming more relevant to our lives every day. Every time our email inbox sorts spam emails, there is a machine learning model. When we use voice recognition on our phones, neural networks sort and analyze our words. This book will give you the key terms and basic understanding of the fastest-growing field in computer science as well as: A breakdown of machine learning techniques and algorithms; why and how they are used The tools you will need. Where to find data, what languages work best for machine learning, and what technology is available to help you. Practical examples of Machine Learning being used in the modern world The basic statistics and mathematics necessary to understand and interpret data A jumping-off point to begin diving into this fascinating technology And Much More!.... Even if you aren't an expert in mathematics or computer programming, you will learn the basics of machine learning from this book. If you are ready to know how machine learning models work, check out this guidebook now to help you get started!...
Principles Of Machine Learning
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Author : Wenmin Wang
language : en
Publisher: Springer Nature
Release Date : 2024-10-26
Principles Of Machine Learning written by Wenmin Wang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-26 with Mathematics categories.
Conducting an in-depth analysis of machine learning, this book proposes three perspectives for studying machine learning: the learning frameworks, learning paradigms, and learning tasks. With this categorization, the learning frameworks reside within the theoretical perspective, the learning paradigms pertain to the methodological perspective, and the learning tasks are situated within the problematic perspective. Throughout the book, a systematic explication of machine learning principles from these three perspectives is provided, interspersed with some examples. The book is structured into four parts, encompassing a total of fifteen chapters. The inaugural part, titled “Perspectives,” comprises two chapters: an introductory exposition and an exploration of the conceptual foundations. The second part, “Frameworks”: subdivided into five chapters, each dedicated to the discussion of five seminal frameworks: probability, statistics, connectionism, symbolism, and behaviorism. Continuing further, the third part, “Paradigms,” encompasses four chapters that explain the three paradigms of supervised learning, unsupervised learning, and reinforcement learning, and narrating several quasi-paradigms emerged in machine learning. Finally, the fourth part, “Tasks”: comprises four chapters, delving into the prevalent learning tasks of classification, regression, clustering, and dimensionality reduction. This book provides a multi-dimensional and systematic interpretation of machine learning, rendering it suitable as a textbook reference for senior undergraduates or graduate students pursuing studies in artificial intelligence, machine learning, data science, computer science, and related disciplines. Additionally, it serves as a valuable reference for those engaged in scientific research and technical endeavors within the realm of machine learning. The translation was done with the help of artificial intelligence. A subsequent human revision was done primarily in terms of content.
Fundamentals Of Reinforcement Learning
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Author : Rafael Ris-Ala
language : en
Publisher: Springer Nature
Release Date : 2023-08-14
Fundamentals Of Reinforcement Learning written by Rafael Ris-Ala and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-14 with Computers categories.
Artificial intelligence (AI) applications bring agility and modernity to our lives, and the reinforcement learning technique is at the forefront of this technology. It can outperform human competitors in strategy games, creative compositing, and autonomous movement. Moreover, it is just starting to transform our civilization. This book provides an introduction to AI, specifies machine learning techniques, and explores various aspects of reinforcement learning, approaching the latest concepts in a didactic and illustrated manner. It is aimed at students who want to be part of technological advances and professors engaged in the development of innovative applications, helping with academic and industrial challenges. Understanding the Fundamentals of Reinforcement Learning will allow you to: Understand essential AI concepts Gain professional experience Interpret sequential decision problems and solve them with reinforcement learning Learn how the Q-Learning algorithm works Practice with commented Python code Find advantageous directions
Artificial Intelligence And Machine Learning Essentials
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Author : Kiran Kumar Pappula
language : en
Publisher: Academic Guru Publishing House
Release Date : 2025-02-06
Artificial Intelligence And Machine Learning Essentials written by Kiran Kumar Pappula and has been published by Academic Guru Publishing House this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-06 with Study Aids categories.
Artificial Intelligence and Machine Learning Essentials is a comprehensive guide tailored for beginners and early-stage learners eager to explore the fascinating world of Al and ML. The book covers key concepts, techniques, and tools across eight well-structured chapters, offering readers a clear pathway from fundamental understanding to practical knowledge. Beginning with the basics of Artificial Intelligence, the book introduces readers to its history, types, and applications across different industries. It then delves into the core principles of Machine Learning, detailing the various types, algorithms, and workflows essential for building intelligent systems. Readers will gain insights into critical data preprocessing techniques that ensure high-quality input for ML models. The book further explores popular supervised and unsupervised learning algorithms, including linear regression, decision trees, K-means, and PCA, making it easier to grasp both the theoretical and practical aspects. Reinforcement Learning, Deep Learning models like CNNs and RNNs, and Natural Language Processing techniques are also thoroughly explained with real-life relevance. Written in simple and accessible language, the book makes complex topics easy to understand, making it suitable for university students, tech enthusiasts, and professionals from non-technical backgrounds. With a strong emphasison clarity and practical understanding, this book serves as a stepping stone into one of the most promising areas of modern technology.
Machine Learning And Artificial Intelligence In Radiation Oncology
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Author : Barry S. Rosenstein
language : en
Publisher: Academic Press
Release Date : 2023-12-02
Machine Learning And Artificial Intelligence In Radiation Oncology written by Barry S. Rosenstein and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-02 with Science categories.
Machine Learning and Artificial Intelligence in Radiation Oncology: A Guide for Clinicians is designed for the application of practical concepts in machine learning to clinical radiation oncology. It addresses the existing void in a resource to educate practicing clinicians about how machine learning can be used to improve clinical and patient-centered outcomes. This book is divided into three sections: the first addresses fundamental concepts of machine learning and radiation oncology, detailing techniques applied in genomics; the second section discusses translational opportunities, such as in radiogenomics and autosegmentation; and the final section encompasses current clinical applications in clinical decision making, how to integrate AI into workflow, use cases, and cross-collaborations with industry. The book is a valuable resource for oncologists, radiologists and several members of biomedical field who need to learn more about machine learning as a support for radiation oncology. - Presents content written by practicing clinicians and research scientists, allowing a healthy mix of both new clinical ideas as well as perspectives on how to translate research findings into the clinic - Provides perspectives from artificial intelligence (AI) industry researchers to discuss novel theoretical approaches and possibilities on academic collaborations - Brings diverse points-of-view from an international group of experts to provide more balanced viewpoints on a complex topic
Fundamentals Of Artificial Intelligence
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Author : Nisha Talagala
language : en
Publisher:
Release Date : 2022
Fundamentals Of Artificial Intelligence written by Nisha Talagala and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
This book is for K12 students who want to learn AI, for teachers who want to teach AI and bring AI into the classroom, and for any individual who wants to understand AI in a simple and effective way.Artificial Intelligence is all around us. This book demystifies AI for K12 students and teachers using a unique combination of concept learning, hands-on plugged and unplugged exercises, context of how AI is used in industries from finance to marketing, and project ideas for students to apply their own creativity and build their own AIs. The ten fully illustrated color chapters cover both Machine Learning and Deep Learning, a comprehensive overview of AI Ethics, and popular AI algorithms from Linear Regression to Convolutional Neural Networks. Teacher's corners provide teachers with additional resources for bringing AI into the classroom. The book is paired with extensive online resources in curriculum, datasets, exercises, and code.The two authors (Nisha Talagala and Sindhu Ghanta) have extensive experience building industry AI solutions and have applied their knowledge to teach AI to K12 students. This book comes from their experiences of teaching AI to thousands of students around the world.
Fundamentals Of Machine Learning For Predictive Data Analytics Second Edition
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Author : John D. Kelleher
language : en
Publisher: MIT Press
Release Date : 2020-10-20
Fundamentals Of Machine Learning For Predictive Data Analytics Second Edition written by John D. Kelleher and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-20 with Computers categories.
The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning. The book is accessible, offering nontechnical explanations of the ideas underpinning each approach before introducing mathematical models and algorithms. It is focused and deep, providing students with detailed knowledge on core concepts, giving them a solid basis for exploring the field on their own. Both early chapters and later case studies illustrate how the process of learning predictive models fits into the broader business context. The two case studies describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book can be used as a textbook at the introductory level or as a reference for professionals.