Machine Learning For Absolute Beginners A Step By Step Guide Algorithms For Supervised And Unsupervised Learning With Real World Applications
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Machine Learning For Absolute Beginners A Step By Step Guide Algorithms For Supervised And Unsupervised Learning With Real World Applications
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Author : Raymond Kazuya
language : en
Publisher: Raymond Kazuya
Release Date : 2017-12-17
Machine Learning For Absolute Beginners A Step By Step Guide Algorithms For Supervised And Unsupervised Learning With Real World Applications written by Raymond Kazuya and has been published by Raymond Kazuya this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12-17 with Comics & Graphic Novels categories.
" Machines can LEARN ?!?! Machine learning occurs primarily through the use of " algorithms" and other elaborate procedures. Whether you're a novice, intermediate or expert this book will teach you all the ins, outs and everything you need to know about machine learning. Instead of spending hundreds or even thousands of dollars on courses/materials why not read this book instead? Its a worthwhile read and the most valuable investment you can make for yourself. What You'll Learn - Supervised Learning -Unsupervised Learning -Reinforced Learning - Algorithms -Decision Tree -Random Forest -Neural Networks -Python -Deep Learning -And much, much more! ☆★☆ Grab your copy now! ☆★☆ "
Machine Learning For Beginners 2025 Step By Step Guide To Master Ml Algorithms Real World Applications
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Author : J. Paaul
language : en
Publisher: Code Academy
Release Date : 2025-05-07
Machine Learning For Beginners 2025 Step By Step Guide To Master Ml Algorithms Real World Applications written by J. Paaul and has been published by Code Academy this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-07 with Computers categories.
Machine Learning for Beginners 2025 is the perfect guide for anyone looking to dive into the world of machine learning. This book breaks down complex concepts into easy-to-understand explanations and hands-on examples. Covering the fundamentals of ML algorithms, data preprocessing, model evaluation, and real-world applications, this book is ideal for newcomers to the field. With practical projects and step-by-step tutorials, readers will gain the skills to implement machine learning models using Python and popular libraries like Scikit-learn and TensorFlow, making this a comprehensive resource for aspiring data scientists.
Machine Learning For Absolute Beginners
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Author : Jonathan K. Hari
language : en
Publisher: epubli
Release Date : 2025-06-23
Machine Learning For Absolute Beginners written by Jonathan K. Hari and has been published by epubli this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-23 with Computers categories.
Machine Learning for Absolute Beginners In a world where artificial intelligence is shaping the future, understanding machine learning is no longer optional—it's essential. This book takes you on a journey through the fundamentals of AI and data science, breaking down complex concepts into simple, beginner-friendly language. Whether you're a tech enthusiast, an aspiring data scientist, or just curious about the power of AI, this step-by-step guide will help you grasp the essentials without needing any prior coding experience. Inside This Book, You'll Discover: The difference between AI, machine learning, and deep learning Types of machine learning: Supervised, unsupervised, and reinforcement learning Key concepts like data, features, labels, and models Understanding algorithms, from linear regression to neural networks How to prepare and clean data for machine learning An introduction to Python and popular ML libraries like Scikit-Learn, TensorFlow, and PyTorch Real-world applications and ethical considerations in AI This book is designed for absolute beginners, ensuring you build a strong foundation in machine learning without the need for complex mathematics or programming skills. If you've ever wanted to explore AI but didn't know where to start—this is your gateway into the exciting world of machine learning! Scroll Up and Grab Your Copy Today!
Understanding Machine Learning
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Author : Shai Shalev-Shwartz
language : en
Publisher: Cambridge University Press
Release Date : 2014-05-19
Understanding Machine Learning written by Shai Shalev-Shwartz and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-19 with Computers categories.
Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
Text Book Of Artificial Intelligence
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Author : Dr. Rakesh Singh, Dr. Shuchi Dave, Prof. Sushil K. Kashaw, Prof. (Dr.) Sandeep Gangrade, Lalbihari Barik
language : en
Publisher: Shashwat Publication
Release Date : 2025-04-25
Text Book Of Artificial Intelligence written by Dr. Rakesh Singh, Dr. Shuchi Dave, Prof. Sushil K. Kashaw, Prof. (Dr.) Sandeep Gangrade, Lalbihari Barik and has been published by Shashwat Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-25 with Fiction categories.
Textbook of Artificial Intelligence is a comprehensive guide for students, educators, and professionals seeking foundational and advanced knowledge in AI. It begins with a clear definition and history of Artificial Intelligence, helping readers understand its roots and evolution. The book explores real-world applications of AI across various industries including healthcare, finance, education, and autonomous systems. Core AI branches like Machine Learning, Deep Learning, NLP, Robotics, and Computer Vision are introduced with practical insights. In-depth coverage of Intelligent Agents explains their structure, types, and operating environments. The Problem Solving section walks readers through classic algorithms like BFS, DFS, A*, and adversarial search techniques. Knowledge Representation and Reasoning introduces propositional logic, predicate logic, semantic nets, and uncertainty models like Bayesian networks. Machine Learning fundamentals cover supervised, unsupervised, and reinforcement learning, alongside key algorithms and neural networks. Advanced topics like CNNs, RNNs, Transformers, GANs, and NLP tasks are well-structured for deeper understanding. Dedicated chapters on AI in real-world applications showcase use cases in robotics, vision, and recommender systems. Hands-on tools like TensorFlow, PyTorch, Keras, and data handling with Pandas and NumPy are introduced for practical learning. The book encourages ethical thinking with discussions on AI fairness, privacy, transparency, and regulation. A special focus on the future of AI covers trends like generative models, autonomous agents, and human-AI collaboration. Well-organized content helps learners connect theory to practical implementation and innovation. Step-by-step examples and algorithm breakdowns make complex topics easy to understand. Each chapter includes conceptual summaries, illustrations, and review questions for better retention. Perfect for beginners and intermediate learners, as well as educators designing AI curricula. Prepares students for research and industry careers with real-world insights and project ideas. Bridges the gap between traditional AI principles and modern AI technologies. A valuable reference for anyone passionate about building intelligent systems and exploring the world of AI.
Machine Learning For Dummies
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Author : John Paul Mueller
language : en
Publisher: John Wiley & Sons
Release Date : 2021-02-09
Machine Learning For Dummies written by John Paul Mueller and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-09 with Computers categories.
One of Mark Cuban’s top reads for better understanding A.I. (inc.com, 2021) Your comprehensive entry-level guide to machine learning While machine learning expertise doesn’t quite mean you can create your own Turing Test-proof android—as in the movie Ex Machina—it is a form of artificial intelligence and one of the most exciting technological means of identifying opportunities and solving problems fast and on a large scale. Anyone who masters the principles of machine learning is mastering a big part of our tech future and opening up incredible new directions in careers that include fraud detection, optimizing search results, serving real-time ads, credit-scoring, building accurate and sophisticated pricing models—and way, way more. Unlike most machine learning books, the fully updated 2nd Edition of Machine Learning For Dummies doesn't assume you have years of experience using programming languages such as Python (R source is also included in a downloadable form with comments and explanations), but lets you in on the ground floor, covering the entry-level materials that will get you up and running building models you need to perform practical tasks. It takes a look at the underlying—and fascinating—math principles that power machine learning but also shows that you don't need to be a math whiz to build fun new tools and apply them to your work and study. Understand the history of AI and machine learning Work with Python 3.8 and TensorFlow 2.x (and R as a download) Build and test your own models Use the latest datasets, rather than the worn out data found in other books Apply machine learning to real problems Whether you want to learn for college or to enhance your business or career performance, this friendly beginner's guide is your best introduction to machine learning, allowing you to become quickly confident using this amazing and fast-developing technology that's impacting lives for the better all over the world.
Interpretable Machine Learning
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Author : Christoph Molnar
language : en
Publisher: Lulu.com
Release Date : 2020
Interpretable Machine Learning written by Christoph Molnar and has been published by Lulu.com this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Computers categories.
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Choosing Chinese Universities
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Author : Alice Y.C. Te
language : en
Publisher: Routledge
Release Date : 2022-10-07
Choosing Chinese Universities written by Alice Y.C. Te and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-07 with Education categories.
This book unpacks the complex dynamics of Hong Kong students’ choice in pursuing undergraduate education at the universities of Mainland China. Drawing on an empirical study based on interviews with 51 students, this book investigates how macro political/economic factors, institutional influences, parental influence, and students’ personal motivations have shaped students’ eventual choice of university. Building on Perna’s integrated model of college choice and Lee’s push-pull mobility model, this book conceptualizes that students’ border crossing from Hong Kong to Mainland China for higher education is a trans-contextualized negotiated choice under the "One Country, Two Systems" principle. The findings reveal that during the decision-making process, influencing factors have conditioned four archetypes of student choice: Pragmatists, Achievers, Averages, and Underachievers. The book closes by proposing an enhanced integrated model of college choice that encompasses both rational motives and sociological factors, and examines the theoretical significance and practical implications of the qualitative study. With its focus on student choice and experiences of studying in China, this book’s research and policy findings will interest researchers, university administrators, school principals, and teachers.
Reinforcement Learning
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Author : Richard S. Sutton
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Reinforcement Learning written by Richard S. Sutton and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.
Reinforcement learning is the learning of a mapping from situations to actions so as to maximize a scalar reward or reinforcement signal. The learner is not told which action to take, as in most forms of machine learning, but instead must discover which actions yield the highest reward by trying them. In the most interesting and challenging cases, actions may affect not only the immediate reward, but also the next situation, and through that all subsequent rewards. These two characteristics -- trial-and-error search and delayed reward -- are the most important distinguishing features of reinforcement learning. Reinforcement learning is both a new and a very old topic in AI. The term appears to have been coined by Minsk (1961), and independently in control theory by Walz and Fu (1965). The earliest machine learning research now viewed as directly relevant was Samuel's (1959) checker player, which used temporal-difference learning to manage delayed reward much as it is used today. Of course learning and reinforcement have been studied in psychology for almost a century, and that work has had a very strong impact on the AI/engineering work. One could in fact consider all of reinforcement learning to be simply the reverse engineering of certain psychological learning processes (e.g. operant conditioning and secondary reinforcement). Reinforcement Learning is an edited volume of original research, comprising seven invited contributions by leading researchers.
Artificial Intelligence And Soft Computing
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Author : Amit Konar
language : en
Publisher: CRC Press
Release Date : 2018-10-08
Artificial Intelligence And Soft Computing written by Amit Konar and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-08 with Technology & Engineering categories.
With all the material available in the field of artificial intelligence (AI) and soft computing-texts, monographs, and journal articles-there remains a serious gap in the literature. Until now, there has been no comprehensive resource accessible to a broad audience yet containing a depth and breadth of information that enables the reader to fully understand and readily apply AI and soft computing concepts. Artificial Intelligence and Soft Computing fills this gap. It presents both the traditional and the modern aspects of AI and soft computing in a clear, insightful, and highly comprehensive style. It provides an in-depth analysis of mathematical models and algorithms and demonstrates their applications in real world problems. Beginning with the behavioral perspective of "human cognition," the text covers the tools and techniques required for its intelligent realization on machines. The author addresses the classical aspects-search, symbolic logic, planning, and machine learning-in detail and includes the latest research in these areas. He introduces the modern aspects of soft computing from first principles and discusses them in a manner that enables a beginner to grasp the subject. He also covers a number of other leading aspects of AI research, including nonmonotonic and spatio-temporal reasoning, knowledge acquisition, and much more. Artificial Intelligence and Soft Computing: Behavioral and Cognitive Modeling of the Human Brain is unique for its diverse content, clear presentation, and overall completeness. It provides a practical, detailed introduction that will prove valuable to computer science practitioners and students as well as to researchers migrating to the subject from other disciplines.