Machine Learning Algorithms For Supervised And Unsupervised Learning
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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.
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 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*
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 : Michael Colins
language : en
Publisher:
Release Date : 2017-07-23
Machine Learning written by Michael Colins and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-23 with Machine learning categories.
We've all heard of AI (artificial intelligence) but what does machine learning really mean? The phrase "Machine Learning" refers to the automatic detection of meaningful data by computing systems. In the last few decades, it has become a common tool in almost any task that needs to understand data from large data sets. One of the biggest application of machine learning technology is the search engine. Search engines learn how to provide the best results based on historic, trending, and relative data sets. When you look at anti-spam software, it learns how to filter email messages. Going to credit cards, transactions are secured by software that knows when fraudulent activities are going on. We currently have digital cameras that detect faces, personal assistant applications that are intelligent enough to learn voice commands. These are all applications based on machine learning! Cars are becoming equipped with accident prevention systems that are powered by machine learning algorithms. Machine learning is also widely used in scientific fields like bioinformatics and astronomy. In contrast to traditional computing, and due to the complexity of patterns that need to be detected, it is hard for a programmer to provide a fine-detailed specification on the execution of these tasks. So where do we start? How about key machine learning algorithms? These are algorithms that are used in the real world, and they give a wide spectrum of the different learning techniques. There are also different algorithms that are better suited for big data. The world has become increasingly connected, and as a result, and in many business applications, there is a lot of data and computation needed to learn different concepts. As you can imagine, the topic of machine learning, depending on the application, can be contained or wildly complex. This book will give you an overview of what machine learning is capable of and some basic algorithms to help you understand the fundamentals of the technology. Finally, how will the employment landscape going to be affected by machine learning in the near future? In later chapters of this book, we will talk about the skills that a you will need to have to work in a profession related to machine learning, and how each field might be affected by the age of computerization. The future is changing very quickly and professionals will need to adapt to ever-evolving technology if they want to stand a chance in keeping up with the joneses.
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
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Author : Morgan Maynard
language : en
Publisher:
Release Date : 2020-05-02
Machine Learning written by Morgan Maynard and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-02 with categories.
How Do Computers Learn? Machine Learning is a branch of computer science that wants to stop programming using a detailed list of commands. Instead, their aim is to implement high-level routines that teach computers how to approach new and unknown problems - these are learning algorithms. In practice, these Algorithms empower computers 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. Unfortunately, the true power and applications of today's Machine Learning Algorithms remain deeply misunderstood by many people. Through this book I want fix this confusion, I want to shed light on the most relevant Machine Learning Algorithms in use today. I will show you exactly how each algorithm works, why it works and when you should use it. Supervised Learning Algorithms K-Nearest Neighbour Naïve Bayes Regressions Unsupervised Learning Algorithms: Support Vector Machines Neural Networks Decision Trees
Kernel Based Algorithms For Mining Huge Data Sets
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Author : Te-Ming Huang
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-03-02
Kernel Based Algorithms For Mining Huge Data Sets written by Te-Ming Huang 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 2006-03-02 with Computers categories.
This is the first book treating the fields of supervised, semi-supervised and unsupervised machine learning collectively. The book presents both the theory and the algorithms for mining huge data sets using support vector machines (SVMs) in an iterative way. It demonstrates how kernel based SVMs can be used for dimensionality reduction and shows the similarities and differences between the two most popular unsupervised techniques.
Machine Learning For Beginners Guide Algorithms
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Author : William Sullivan
language : en
Publisher: PublishDrive
Release Date : 2017-08-19
Machine Learning For Beginners Guide Algorithms 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 2017-08-19 with Computers 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 Note: Bonus chapters included inside! 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 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 for beginners. What You'll Learn Supervised Learning Unsupervised Learning Reinforced Learning Algorithms Decision Tree Random Forest Neural Networks Python Deep Learning And much, much more! This is the most comprehensive and easy to read step by step guide in machine learning that exists. Learn from one of the most reliable programmers alive and expert in the field You do not want to miss out on this incredible offer!
Machine Learning Techniques
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Author : C Perez
language : en
Publisher: Independently Published
Release Date : 2019-05-05
Machine Learning Techniques written by C Perez and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-05 with categories.
Machine learning algorithms use computational methods to "learn" information directly from data without relying on a predetermined equation as a model. The algorithms adaptively improve their performance as the number of samples available for learning increases. Machine learning uses two types of techniques: supervised learning, which trains a model on known input and output data so that it can predict future outputs, and unsupervised learning, which finds hidden patterns or intrinsic structures in input data.The aim of supervised machine learning is to build a model that makes predictions based on evidence in the presence of uncertainty. A supervised learning algorithm takes a known set of input data and known responses to the data (output) and trains a model to generate reasonable predictions for the response to new data. Supervised learning uses classification and regression techniques to develop predictive models.-Classification techniques predict categorical responses, for example, whether an email is genuine or spam, or whether a tumor is cancerous or benign. Classification models classify input data into categories. Typical applications include medical imaging, image and speech recognition, and credit scoring. This book develops Supervised Machine Learning Techniques for Classification.-Regression techniques predict continuous responses, for example, changes in temperature or fluctuations in power demand. Typical applications include electricity load forecasting and algorithmic trading.