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specialists in the field of control, Machine Learning Control by Symbolic Regression opens up a new promising direction of research and acquaints;
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covering deep learning and machine learning basics. It then explains how to use Scala-based ML libraries to solve classification and regression;
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Deploy supervised and unsupervised machine learning algorithms using scikit-learn to perform classification, regression, and clustering;
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), classification, regression and control as the learning tasks. A unified methodology based on the concepts of machine learning, information theory and;
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), classification, regression and control as the learning tasks. A unified methodology based on the concepts of machine learning, information theory and;
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frontier of machine learning, using the caret package in R. Once you develop a familiarity with topics such as the difference between regression;
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utilize previous adaptations in changing environments. Symbolic Visual Learning presents research which adds visual learning capabilities to;
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This book begins with an introduction to AI, followed by machine learning, deep learning, NLP, and reinforcement learning. Readers will;
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and efficient models. Using powerful algorithms and techniques offered by machine learning you can automate any analytical model. This book;
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foundation for your entry into Machine Learning by strengthening your roots (algorithms) with this comprehensive guide. Who This Book Is For;
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learning ecosystem Implement clustering, regression, classification, and neural networks with Go Get to grips with libraries such as Gorgonia;
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Control your machine learning algorithms using test-driven development to achieve quantifiable milestones About This Book * Build;
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generation and forecasting, with a focus on the role and need of Machine Learning in this emerging field of knowledge. Various regression models and;
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An easy-to-follow, step-by-step guide for getting to grips with the real-world application of machine learning algorithms Key Features;
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accuracy of regression and classification models. The parent problem of optimization-centric machine learning is least-squares regression;
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accuracy of regression and classification models. The parent problem of optimization-centric machine learning is least-squares regression;
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Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to;
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With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author;
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by leveraging the power of Machine Learning * A highly practical guide explaining the concepts of problem solving in the easiest possible;
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areas of learning research, computational learning theory, neural network research and symbolic machine learning.;
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supervised machine learning models such as linear regression, logistic regression, decision trees, and random forest. You'll also see unsupervised;
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learning for OpenCV begins by introducing you to the essential concepts of statistical learning, such as classification and regression. Once all;
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Python's rich suite of libraries for statistical modeling Implement regression, clustering and train neural networks from scratch Includes real;
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This book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning;
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