Machine Learning Techniques And Principles
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Machine Learning
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Author : Richard Forsyth
language : en
Publisher: Chapman & Hall
Release Date : 1989
Machine Learning written by Richard Forsyth and has been published by Chapman & Hall this book supported file pdf, txt, epub, kindle and other format this book has been release on 1989 with Computers categories.
Presents results of research into computer systems that can improve their own performance. For undergraduates, graduates, and professionals intending to write or use such systems. The various perspectives of over a dozen contributors are abstracted into the unifying principle: generate + test, which makes possible a provisional taxonomy of machine learning algorithms. The sections cover a background to induction, biologically inspired systems, automated discovery, and long-term perspectives. The paper edition ($29.95) was not seen. Annotation copyrighted by Book News, Inc., Portland, OR
Machine Learning Principles Methods And Techniques
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Author : Madhavi Satish Avhankar
language : en
Publisher: Academic Guru Publishing House
Release Date : 2023-09-04
Machine Learning Principles Methods And Techniques written by Madhavi Satish Avhankar 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 2023-09-04 with Study Aids categories.
Machine learning (ML) is a subfield of AI that helps computers "self-learn" from data sets and improve over time without being programmed in any way. Algorithms that use machine learning can analyse data and figure out what to expect in the future based on what they've learned. In a nutshell, machine learning relies on iterative algorithms and models that improve with practise. In the 1950s, Artificial Intelligence pioneer Arthur Samuel created the first self-learning system for playing checkers, and the phrase "machine learning" was born. He found that the more he used the system, the better it functioned. The development of richer datasets and neural networks have contributed to machine learning's explosive rise in recent years. Machine learning is already integral to almost every aspect of modern life, from automatic translation and picture identification to voice search and self-driving vehicles. This book will describe the process of machine learning in detail.
Machine Learning Principles And Techniques
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Author : Midhun Moorthi C
language : en
Publisher: Academic Guru Publishing House
Release Date : 2023-09-04
Machine Learning Principles And Techniques written by Midhun Moorthi C 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 2023-09-04 with Study Aids categories.
Machine learning has grown to prominence as a beacon of creativity and problem-solving in an era where data is more available than ever before and technology continues to push the limits of what is possible. "Machine Learning Principles and Techniques" offers a thorough and incisive examination of this dynamic and continuously changing area. "Machine Learning Principles and Techniques" is a must-have introduction to the fascinating area of machine learning. This book is your entrance to understanding the essential ideas and practical applications of machine learning, whether you're a newbie eager to unravel the secrets of artificial intelligence or an experienced practitioner looking to improve your abilities.. Beginning with basic notions, this book demystifies complicated issues so that even individuals with no previous knowledge may understand the core of machine learning. It bridges the theoretical and practical gaps by giving hands-on examples and real-world use cases that equip you to solve a wide range of challenges across several domains. The book delves into the mechanics of machine learning, examining algorithms and models ranging from traditional techniques like linear regression to cutting-edge neural networks and deep learning. It goes beyond technical skills to emphasize the ethical aspects necessary for responsible AI development, such as fairness, transparency, and bias reduction.
Machine Learning Techniques And Principles
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Author : Dr. Harshalata J. Petkar
language : en
Publisher: Academic Guru Publishing House
Release Date : 2023-09-04
Machine Learning Techniques And Principles written by Dr. Harshalata J. Petkar 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 2023-09-04 with Study Aids categories.
Machine learning is a branch of AI that seeks to automate repetitive, rule-based tasks by training computers to learn from data sets with little human input. It is a technique for analyzing data that allows for the automated construction of analytical models by drawing on information in numbers, words, hyperlinks, and pictures. Applications that use machine learning take in data, analyze it, and then use automated optimization techniques to increase the precision of their results. In addition to aiding in product creation, machine learning helps businesses keep tabs on shifting client preferences and organizational tendencies. Facebook, Google, and Uber are just a few industry leaders who use machine learning extensively. Machine learning has emerged as a key differentiator for many businesses. When it comes to gathering, analyzing, and reacting to massive volumes of data, Machine Learning is employed extensively across all sectors. In one way or another, Machine Learning affects our everyday lives. The most valuable aspect of machine learning is its ability to make high-quality predictions that may direct wiser choices and prompt more effective actions in real-time with no human involvement.
Text Mining With Machine Learning
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Author : Jan Žižka
language : en
Publisher: CRC Press
Release Date : 2019-10-31
Text Mining With Machine Learning written by Jan Žižka and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-31 with Computers categories.
This book provides a perspective on the application of machine learning-based methods in knowledge discovery from natural languages texts. By analysing various data sets, conclusions which are not normally evident, emerge and can be used for various purposes and applications. The book provides explanations of principles of time-proven machine learning algorithms applied in text mining together with step-by-step demonstrations of how to reveal the semantic contents in real-world datasets using the popular R-language with its implemented machine learning algorithms. The book is not only aimed at IT specialists, but is meant for a wider audience that needs to process big sets of text documents and has basic knowledge of the subject, e.g. e-mail service providers, online shoppers, librarians, etc. The book starts with an introduction to text-based natural language data processing and its goals and problems. It focuses on machine learning, presenting various algorithms with their use and possibilities, and reviews the positives and negatives. Beginning with the initial data pre-processing, a reader can follow the steps provided in the R-language including the subsuming of various available plug-ins into the resulting software tool. A big advantage is that R also contains many libraries implementing machine learning algorithms, so a reader can concentrate on the principal target without the need to implement the details of the algorithms her- or himself. To make sense of the results, the book also provides explanations of the algorithms, which supports the final evaluation and interpretation of the results. The examples are demonstrated using realworld data from commonly accessible Internet sources.
Principles And Labs For Deep Learning
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Author : Shih-Chia Huang
language : en
Publisher: Academic Press
Release Date : 2021-07-06
Principles And Labs For Deep Learning written by Shih-Chia Huang and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-06 with Science categories.
Principles and Labs for Deep Learning provides the knowledge and techniques needed to help readers design and develop deep learning models. Deep Learning techniques are introduced through theory, comprehensively illustrated, explained through the TensorFlow source code examples, and analyzed through the visualization of results. The structured methods and labs provided by Dr. Huang and Dr. Le enable readers to become proficient in TensorFlow to build deep Convolutional Neural Networks (CNNs) through custom APIs, high-level Keras APIs, Keras Applications, and TensorFlow Hub. Each chapter has one corresponding Lab with step-by-step instruction to help the reader practice and accomplish a specific learning outcome. Deep Learning has been successfully applied in diverse fields such as computer vision, audio processing, robotics, natural language processing, bioinformatics and chemistry. Because of the huge scope of knowledge in Deep Learning, a lot of time is required to understand and deploy useful, working applications, hence the importance of this new resource. Both theory lessons and experiments are included in each chapter to introduce the techniques and provide source code examples to practice using them. All Labs for this book are placed on GitHub to facilitate the download. The book is written based on the assumption that the reader knows basic Python for programming and basic Machine Learning. Introduces readers to the usefulness of neural networks and Deep Learning methods Provides readers with in-depth understanding of the architecture and operation of Deep Convolutional Neural Networks Demonstrates the visualization needed for designing neural networks Provides readers with an in-depth understanding of regression problems, binary classification problems, multi-category classification problems, Variational Auto-Encoder, Generative Adversarial Network, and Object detection
Feature Engineering For Machine Learning
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Author : Alice Zheng
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2018-03-23
Feature Engineering For Machine Learning written by Alice Zheng and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-23 with Computers categories.
Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you’ll learn techniques for extracting and transforming features—the numeric representations of raw data—into formats for machine-learning models. Each chapter guides you through a single data problem, such as how to represent text or image data. Together, these examples illustrate the main principles of feature engineering. Rather than simply teach these principles, authors Alice Zheng and Amanda Casari focus on practical application with exercises throughout the book. The closing chapter brings everything together by tackling a real-world, structured dataset with several feature-engineering techniques. Python packages including numpy, Pandas, Scikit-learn, and Matplotlib are used in code examples. You’ll examine: Feature engineering for numeric data: filtering, binning, scaling, log transforms, and power transforms Natural text techniques: bag-of-words, n-grams, and phrase detection Frequency-based filtering and feature scaling for eliminating uninformative features Encoding techniques of categorical variables, including feature hashing and bin-counting Model-based feature engineering with principal component analysis The concept of model stacking, using k-means as a featurization technique Image feature extraction with manual and deep-learning techniques
Principles And Theory For Data Mining And Machine Learning
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Author : Bertrand Clarke
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-07-21
Principles And Theory For Data Mining And Machine Learning written by Bertrand Clarke 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 2009-07-21 with Computers categories.
Extensive treatment of the most up-to-date topics Provides the theory and concepts behind popular and emerging methods Range of topics drawn from Statistics, Computer Science, and Electrical Engineering
Principles Of Data Mining
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Author : David J. Hand
language : en
Publisher: MIT Press
Release Date : 2001-08-17
Principles Of Data Mining written by David J. Hand and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-08-17 with Computers categories.
The first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.
Machine Learning Techniques For Multimedia
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Author : Matthieu Cord
language : en
Publisher: Springer
Release Date : 2014-09-23
Machine Learning Techniques For Multimedia written by Matthieu Cord and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-09-23 with Computers categories.
Processing multimedia content has emerged as a key area for the application of machine learning techniques, where the objectives are to provide insight into the domain from which the data is drawn, and to organize that data and improve the performance of the processes manipulating it. Arising from the EU MUSCLE network, this multidisciplinary book provides a comprehensive coverage of the most important machine learning techniques used and their application in this domain.
