Machine Learning Master Supervised And Unsupervised Learning Algorithms With Real Examples
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Machine Learning Master Supervised And Unsupervised Learning Algorithms With Real Examples
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Author : Ruchi Doshi
language : en
Publisher:
Release Date : 2022
Machine Learning Master Supervised And Unsupervised Learning Algorithms With Real Examples written by Ruchi Doshi 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.
Machine Learning Foundations
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Author : Taeho Jo
language : en
Publisher: Springer Nature
Release Date : 2021-02-12
Machine Learning Foundations written by Taeho Jo and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-12 with Technology & Engineering categories.
This book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning techniques. The book consists of four parts: foundation, supervised learning, unsupervised learning, and advanced learning. The first part provides the fundamental materials, background, and simple machine learning algorithms, as the preparation for studying machine learning algorithms. The second and the third parts provide understanding of the supervised learning algorithms and the unsupervised learning algorithms as the core parts. The last part provides advanced machine learning algorithms: ensemble learning, semi-supervised learning, temporal learning, and reinforced learning. Provides comprehensive coverage of both learning algorithms: supervised and unsupervised learning; Outlines the computation paradigm for solving classification, regression, and clustering; Features essential techniques for building the a new generation of machine learning.
Hands On Unsupervised Learning With Python
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Author : Giuseppe Bonaccorso
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-02-28
Hands On Unsupervised Learning With Python written by Giuseppe Bonaccorso 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 2019-02-28 with Computers categories.
Discover the skill-sets required to implement various approaches to Machine Learning with Python Key FeaturesExplore unsupervised learning with clustering, autoencoders, restricted Boltzmann machines, and moreBuild your own neural network models using modern Python librariesPractical examples show you how to implement different machine learning and deep learning techniquesBook Description Unsupervised learning is about making use of raw, untagged data and applying learning algorithms to it to help a machine predict its outcome. With this book, you will explore the concept of unsupervised learning to cluster large sets of data and analyze them repeatedly until the desired outcome is found using Python. This book starts with the key differences between supervised, unsupervised, and semi-supervised learning. You will be introduced to the best-used libraries and frameworks from the Python ecosystem and address unsupervised learning in both the machine learning and deep learning domains. You will explore various algorithms, techniques that are used to implement unsupervised learning in real-world use cases. You will learn a variety of unsupervised learning approaches, including randomized optimization, clustering, feature selection and transformation, and information theory. You will get hands-on experience with how neural networks can be employed in unsupervised scenarios. You will also explore the steps involved in building and training a GAN in order to process images. By the end of this book, you will have learned the art of unsupervised learning for different real-world challenges. What you will learnUse cluster algorithms to identify and optimize natural groups of dataExplore advanced non-linear and hierarchical clustering in actionSoft label assignments for fuzzy c-means and Gaussian mixture modelsDetect anomalies through density estimationPerform principal component analysis using neural network modelsCreate unsupervised models using GANsWho this book is for This book is intended for statisticians, data scientists, machine learning developers, and deep learning practitioners who want to build smart applications by implementing key building block unsupervised learning, and master all the new techniques and algorithms offered in machine learning and deep learning using real-world examples. Some prior knowledge of machine learning concepts and statistics is desirable.
Supervised Learning And Its Applications
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Author : Rakesh Kumar
language : en
Publisher: Independently Published
Release Date : 2024-12
Supervised Learning And Its Applications written by Rakesh Kumar and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12 with Computers categories.
In an age where data is considered the new oil, the ability to harness and utilize this data effectively has become a critical driver of progress across industries. At the heart of this data revolution lies the concept of machine learning, a field of artificial intelligence that empowers systems to learn and make decisions from data without explicit programming. Among the various paradigms of machine learning, supervised learning stands out as one of the most powerful and widely used approaches. Supervised learning is a method where machines learn from labeled datasets to predict outcomes or classify data into predefined categories. It has revolutionized industries by enabling predictive analytics, enhancing decision-making processes, and creating intelligent systems capable of performing tasks previously thought to be exclusively human. From predicting customer behavior to diagnosing diseases and powering self-driving cars, supervised learning has become a cornerstone of modern technological advancements. This book, Supervised Learning and Its Applications, is designed to provide a comprehensive understanding of supervised learning, from its mathematical foundations to practical applications in diverse domains. Whether you are a data science enthusiast, a student embarking on your machine learning journey, or a professional seeking to deepen your knowledge, this book serves as both a guide and a resource to help you master supervised learning. What This Book Covers The journey through this book begins with an overview of machine learning and the distinction between supervised and unsupervised learning paradigms. We then dive into the key concepts and mathematics underpinning supervised learning, including linear algebra, probability, and optimization techniques, which form the foundation of its algorithms. Subsequent chapters explore the most popular supervised learning algorithms, such as linear regression, logistic regression, decision trees, support vector machines, and neural networks. For each algorithm, we explain its mechanics, strengths, limitations, and real-world applications, accompanied by practical examples and Python code implementations. Recognizing that data preparation is critical to the success of machine learning models, we dedicate chapters to feature engineering, handling imbalanced datasets, and ensuring data quality. We also discuss how to evaluate and fine-tune models using metrics, cross-validation techniques, and hyperparameter optimization. The latter part of the book delves into the applications of supervised learning in various industries, from business and healthcare to technology and environmental studies. Case studies illustrate how supervised learning is applied in real-world scenarios, providing readers with insights into its transformative potential. We also explore advanced topics, such as deep learning for supervised tasks, transfer learning, and the ethical considerations that come with building machine learning models, including bias, privacy concerns, and explainability. Finally, we look to the future, discussing emerging trends and the role of cutting-edge technologies like quantum computing in supervised learning. Why This Book Matters Supervised learning is no longer just a tool for data scientists-it has become a critical skill in an era where data-driven decision-making is paramount. Understanding how to apply supervised learning effectively can open doors to solving complex problems, optimizing processes, and creating value in innovative ways. As you progress through this book, you will gain not only a theoretical understanding of supervised learning but also practical skills to implement it in real-world scenarios. By bridging the gap between theory and practice, this book equips you to tackle challenges, drive innovation, and contribute to the evolving landscape of AI ML.
Kickstart Unsupervised Machine Learning Master Unsupervised Machine Learning Through Pattern Discovery Clustering And Dimensionality Reduction To Build Intelligent Real World Applications
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Author : Dr. Nimrita
language : en
Publisher: Orange Education Pvt Limited
Release Date : 2025-12-27
Kickstart Unsupervised Machine Learning Master Unsupervised Machine Learning Through Pattern Discovery Clustering And Dimensionality Reduction To Build Intelligent Real World Applications written by Dr. Nimrita and has been published by Orange Education Pvt Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-12-27 with Computers categories.
Unlock the power of unsupervised learning to uncover hidden insights and transform raw data into actionable knowledge. Key Features● Master unsupervised learning techniques for Machine Learning with real-world applications.● Learn clustering, dimensionality reduction, and anomaly detection with real-world applications.● Build practical expertise through step-by-step coding and practical examples as well as datasets. Book DescriptionUnsupervised machine learning is revolutionizing how organizations extract value from raw data, revealing patterns and structures without predefined labels. From customer segmentation and fraud detection to generative modeling, its versatility drives innovation across industries. Kickstart Unsupervised Machine Learning is your comprehensive companion to mastering this transformative field. Starting with the core principles, the book introduces essential clustering algorithms—including K-Means, DBSCAN, and hierarchical approaches—before advancing to dimensionality reduction techniques such as PCA, t-SNE, and UMAP for simplifying complex data. It then explores sophisticated models like Gaussian Mixture Models and Generative Adversarial Networks (GANs), combining theory with practical coding exercises and hands-on projects using real-world datasets to solidify your understanding. Thus, by the end of this book, you will confidently evaluate, deploy, and optimize unsupervised models to derive meaningful insights from unstructured data. What you will learn● Understand the principles and algorithms of unsupervised learning from ground-up.● Apply clustering and dimensionality reduction techniques on complex datasets.● Evaluate and visualize models using key performance metrics such as validation and interpretability.● Implement unsupervised workflows using Python and open datasets.● Solve real-world challenges in NLP, image, and anomaly detection.● Extend learning methods to research and production-level projects.
Supervised And Unsupervised Learning For Data Science
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Author : Michael W. Berry
language : en
Publisher: Springer Nature
Release Date : 2019-09-04
Supervised And Unsupervised Learning For Data Science written by Michael W. Berry and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-04 with Technology & Engineering categories.
This book covers the state of the art in learning algorithms with an inclusion of semi-supervised methods to provide a broad scope of clustering and classification solutions for big data applications. Case studies and best practices are included along with theoretical models of learning for a comprehensive reference to the field. The book is organized into eight chapters that cover the following topics: discretization, feature extraction and selection, classification, clustering, topic modeling, graph analysis and applications. Practitioners and graduate students can use the volume as an important reference for their current and future research and faculty will find the volume useful for assignments in presenting current approaches to unsupervised and semi-supervised learning in graduate-level seminar courses. The book is based on selected, expanded papers from the Fourth International Conference on Soft Computing in Data Science (2018). Includes new advances in clustering and classification using semi-supervised and unsupervised learning; Address new challenges arising in feature extraction and selection using semi-supervised and unsupervised learning; Features applications from healthcare, engineering, and text/social media mining that exploit techniques from semi-supervised and unsupervised learning.
Machine Learning
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Author : Joshua Chapmann
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2017-06-26
Machine Learning written by Joshua Chapmann and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-06-26 with categories.
Computers can't LEARN... Right?! Machine Learning is a branch of computer science that wants to stop programming computers using a list of detailed instructions and instead use a set of high-level commands which they can apply to many unknown scenarios - these are called algorithms. In practice, they want to give computers the ability to Learn and to ADAPT. We can use these algorithms to obtain insights, recognize patterns and make predictions from data, images, sounds or videos we have never seen before (or even knew existed). Unfortunately, the true power and applications of today's Machine Learning Algorithms is misunderstood by most people. Through this book I want fix this confusion, I want to shed light on the most relevant Machine Learning Algorithms used in the industry: Supervised Learning Algorithms K-Nearest Neighbour Na�ve Bayes Regressions Unsupervised Learning Algorithms: Support Vector Machines Decision Trees
Machine Learning Demystified A Practical Guide To Building Smarter Systems
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Author : Guillaume Lessard
language : en
Publisher: iD01t Productions
Release Date : 2025-01-14
Machine Learning Demystified A Practical Guide To Building Smarter Systems written by Guillaume Lessard and has been published by iD01t Productions this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-14 with Computers categories.
Machine Learning Demystified: A Practical Guide to Building Smarter Systems Unlock the power of Artificial Intelligence with Machine Learning Demystified, a comprehensive and beginner-friendly guide to understanding and applying machine learning principles in real-world scenarios. Authored by Guillaume Lessard, this book bridges the gap between complex algorithms and practical applications, making AI accessible to everyone—from curious beginners to seasoned professionals. What Awaits Inside? Simplified Learning: Master the fundamentals of machine learning with relatable examples and step-by-step tutorials. Algorithm Essentials: Explore supervised and unsupervised learning, neural networks, natural language processing, and computer vision. Hands-On Projects: Dive into real-world applications like spam detection, housing price prediction, and image recognition systems. Practical Tools: Gain expertise in industry-standard frameworks such as TensorFlow, PyTorch, and Scikit-Learn. Ethical AI Practices: Learn to build responsible AI systems with fairness, transparency, and privacy in mind. Why Choose This Book? Many AI resources overwhelm readers with heavy mathematics and jargon. This guide adopts a unique approach, focusing on practical, actionable insights that empower you to create smarter systems. Whether you’re exploring AI's potential for business or personal projects, this book provides the clarity and confidence needed to succeed. Who Is It For? Aspiring data scientists and developers Business professionals exploring AI-driven solutions Anyone passionate about the future of technology Embark on a journey to demystify machine learning and turn your ideas into reality. Your smarter systems start here!
Machine Learning Algorithms For Supervised And Unsupervised Learning
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Author : William Sullivan
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2018-03-06
Machine Learning Algorithms For Supervised And Unsupervised Learning written by William Sullivan and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-06 with categories.
Machine Learning Second Edition I listened carefully to feedback from customers for my original book, and revamped this new edition. I'm excited to present you the second edition with various high quality diagrams, explanations, extensive information and so much more value packed within. What You'll Learn... Supervised Learning Unsupervised Learning Reinforced Learning Algorithms Decision Tree Random Forest And much, much more! Don't miss out on this opportunity to expand your knowledge base with the second edition of my original machine learning book in the artificial intelligence series. Other books easily retail for $50-$100+ and have far less quality content. This book is by far superior and exceeds any other book available. Make the greatest investment in yourself by investing in your knowledge! Buy Now *Note: For the best visual experience of diagrams it is highly recommend you purchase the paperback version*
Mastering Machine Learning Algorithms
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Author : Giuseppe Bonaccorso
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-05-25
Mastering Machine Learning Algorithms written by Giuseppe Bonaccorso 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 2018-05-25 with Computers categories.
Explore and master the most important algorithms for solving complex machine learning problems. Key Features Discover high-performing machine learning algorithms and understand how they work in depth. One-stop solution to mastering supervised, unsupervised, and semi-supervised machine learning algorithms and their implementation. Master concepts related to algorithm tuning, parameter optimization, and more Book Description Machine learning is a subset of AI that aims to make modern-day computer systems smarter and more intelligent. The real power of machine learning resides in its algorithms, which make even the most difficult things capable of being handled by machines. However, with the advancement in the technology and requirements of data, machines will have to be smarter than they are today to meet the overwhelming data needs; mastering these algorithms and using them optimally is the need of the hour. Mastering Machine Learning Algorithms is your complete guide to quickly getting to grips with popular machine learning algorithms. You will be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and will learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this book will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries such as scikit-learn. You will also learn how to use Keras and TensorFlow to train effective neural networks. If you are looking for a single resource to study, implement, and solve end-to-end machine learning problems and use-cases, this is the book you need. What you will learn Explore how a ML model can be trained, optimized, and evaluated Understand how to create and learn static and dynamic probabilistic models Successfully cluster high-dimensional data and evaluate model accuracy Discover how artificial neural networks work and how to train, optimize, and validate them Work with Autoencoders and Generative Adversarial Networks Apply label spreading and propagation to large datasets Explore the most important Reinforcement Learning techniques Who this book is for This book is an ideal and relevant source of content for data science professionals who want to delve into complex machine learning algorithms, calibrate models, and improve the predictions of the trained model. A basic knowledge of machine learning is preferred to get the best out of this guide.