Supervised And Unsupervised Learning For Data Science
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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
A Survey Of Machine Learning Models For Prediabetes Screening
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Author : Amos Olwendo
language : en
Publisher: GRIN Verlag
Release Date : 2025-03-13
A Survey Of Machine Learning Models For Prediabetes Screening written by Amos Olwendo and has been published by GRIN Verlag this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-13 with Computers categories.
Scientific Study from the year 2025 in the subject Communications - Multimedia, Internet, New Technologies, grade: 18.0, Kenyatta University, language: English, abstract: Diabetes is gradually becoming a global challenge owing to the gradual increase in the number of cases of Type 2 diabetes mellitus (T2DM). T2DM is characterized as a state of hyperglycaemia due to abnormal control of insulin levels that eventually affects metabolism. This study aimed to review articles that implement machine learning methods to identify suitable risk factors for prediabetes. The study adopted the preferred reporting items for systematic review (PRISMA) protocol and research questions were formulated by the identification of synonyms and related terms "predictors and prediabetes and machine learning" from PubMed and Google scholar. Both observational and interventional original articles that were published between 2018 and 2023 were included in this study. Eligibility for inclusion was determined by scanning the article title, abstract, and study methodology section.
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.
Supervised Learning With Python
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Author : Vaibhav Verdhan
language : en
Publisher: Apress
Release Date : 2020-10-08
Supervised Learning With Python written by Vaibhav Verdhan and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-08 with Computers categories.
Gain a thorough understanding of supervised learning algorithms by developing use cases with Python. You will study supervised learning concepts, Python code, datasets, best practices, resolution of common issues and pitfalls, and practical knowledge of implementing algorithms for structured as well as text and images datasets. You’ll start with an introduction to machine learning, highlighting the differences between supervised, semi-supervised and unsupervised learning. In the following chapters you’ll study regression and classification problems, mathematics behind them, algorithms like Linear Regression, Logistic Regression, Decision Tree, KNN, Naïve Bayes, and advanced algorithms like Random Forest, SVM, Gradient Boosting and Neural Networks. Python implementation is provided for all the algorithms. You’ll conclude with an end-to-end model development process including deployment and maintenance of the model. After reading Supervised Learning with Python you’ll have a broad understanding of supervised learning and its practical implementation, and be able to run the code and extend it in an innovative manner. What You'll Learn Review the fundamental building blocks and concepts of supervised learning using Python Develop supervised learning solutions for structured data as well as text and images Solve issues around overfitting, feature engineering, data cleansing, and cross-validation for building best fit models Understand the end-to-end model cycle from business problem definition to model deployment and model maintenance Avoid the common pitfalls and adhere to best practices while creating a supervised learning model using Python Who This Book Is For Data scientists or data analysts interested in best practices and standards for supervised learning, and using classification algorithms and regression techniques to develop predictive models.
Astronomical Data Analysis Software And Systems Xii
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Author : Harry Edward Payne
language : en
Publisher:
Release Date : 2003
Astronomical Data Analysis Software And Systems Xii written by Harry Edward Payne and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with Astronomy categories.
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.
Markov Models Supervised And Unsupervised Machine Learning Mastering Data Science And Python
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Author : William Sullivan
language : en
Publisher: PublishDrive
Release Date : 2019-04-29
Markov Models Supervised And Unsupervised Machine Learning Mastering Data Science And Python written by William Sullivan and has been published by PublishDrive this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-29 with Young Adult Nonfiction categories.
Markov Models Supervised and Unsupervised Machine Learning: Mastering Data Science & Python o you want to MASTER Data science? Understand Markov Models and learn the real world application to accurately predict future events. Extend your knowledge of machine learning, python programming & algorithms. What you'll Learn · Mathematics Behind Markov Algorithms · 3 Main Problems Of Markov Models And How To Overcome Them · Uses And Applications For Machine Learning · Python Programming · Speech Recognition · Weather Reporting · The Markov Rule And Markov's Model · Fundamental Axioms Of Statistics And Probability · Solutions · Theories · Artificial Intelligence · Bayesian Inference · Important Tools Used With HMM · And Much, Much, More! The objective of this book is to teach you the essentials at the most fundamental level. You will learn the ins and outs of machine learning, and its real world applications. Also, specifically you will discover practical implementations of Markov Models in python programming. This book offers high value and is the greatest investment in your knowledge base you can make that will benefit you in the long run. Why not take this opportunity to take advantage now and get ahead of everyone else? Other books can easily retail for $100s- $1000s of dollars! Get equipped with the knowledge you need to advance yourself today at an affordable price. What are you waiting for? Don't miss out on this opportunity! Grab Your Copy Now!
Business Data Science Combining Machine Learning And Economics To Optimize Automate And Accelerate Business Decisions
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Author : Matt Taddy
language : en
Publisher: McGraw Hill Professional
Release Date : 2019-08-23
Business Data Science Combining Machine Learning And Economics To Optimize Automate And Accelerate Business Decisions written by Matt Taddy and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-23 with Business & Economics categories.
Use machine learning to understand your customers, frame decisions, and drive value The business analytics world has changed, and Data Scientists are taking over. Business Data Science takes you through the steps of using machine learning to implement best-in-class business data science. Whether you are a business leader with a desire to go deep on data, or an engineer who wants to learn how to apply Machine Learning to business problems, you’ll find the information, insight, and tools you need to flourish in today’s data-driven economy. You’ll learn how to: Use the key building blocks of Machine Learning: sparse regularization, out-of-sample validation, and latent factor and topic modeling Understand how use ML tools in real world business problems, where causation matters more that correlation Solve data science programs by scripting in the R programming language Today’s business landscape is driven by data and constantly shifting. Companies live and die on their ability to make and implement the right decisions quickly and effectively. Business Data Science is about doing data science right. It’s about the exciting things being done around Big Data to run a flourishing business. It’s about the precepts, principals, and best practices that you need know for best-in-class business data science.
Tsinghua Science And Technology
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Author :
language : en
Publisher:
Release Date : 2004
Tsinghua Science And Technology written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Science categories.