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This book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning;
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Implement machine learning algorithms to build ensemble models using Keras, H2O, Scikit-Learn, Pandas and more Key Features Apply popular;
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Deploy supervised and unsupervised machine learning algorithms using scikit-learn to perform classification, regression, and clustering;
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Work smarter with machine learning! Begin with core machine learning concepts-types of learning, algorithms, data preparation, and more;
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Learn how to apply test-driven development (TDD) to machine-learning algorithms - and catch mistakes that could sink your analysis. In this;
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Based on the author's experience in teaching data science for more than 10 years, Mathematics and Programming for Machine Learning with R;
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Based on the author's experience in teaching data science for more than 10 years, Mathematics and Programming for Machine Learning with R;
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With the flexibility and features of scikit-learn and Python, build machine learning algorithms that optimize the programming process and;
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learning, including those that are solving technology requirements, evaluation of methodology advances and algorithm demonstrations. The intent;
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learning algorithms. In spite of the intensive research and development in this area, there does not exist a systematic treatment to introduce the;
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learning algorithms. In spite of the intensive research and development in this area, there does not exist a systematic treatment to introduce the;
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Quantum-enhanced machine learning refers to quantum algorithms that solve tasks in machine learning, thereby improving a classical machine;
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This book presents studies involving algorithms in the machine learning paradigms. It discusses a variety of learning problems with diverse;
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This book presents studies involving algorithms in the machine learning paradigms. It discusses a variety of learning problems with diverse;
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in conjunction with 16th edition of ICDCIT. The book covers topics, like machine learning and its applications, statistical learning, neural network;
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This book on optimization includes forewords by Michael I. Jordan, Zongben Xu and Zhi-Quan Luo. Machine learning relies heavily on;
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This book on optimization includes forewords by Michael I. Jordan, Zongben Xu and Zhi-Quan Luo. Machine learning relies heavily on;
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Python makes machine learning easy for beginners and experienced developers With computing power increasing exponentially and costs;
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Explore and master the most important algorithms for solving complex machine learning problems. Key Features Discover high-performing;
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Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide About This;
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Examine the latest technological advancements in building a scalable machine-learning model with big data using R. This second edition;
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This book presents the state of the art in distributed machine learning algorithms that are based on gradient optimization methods. In the;
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book will give you the confidence and skills when developing all the major machine learning models. In Pro Machine Learning Algorithms, you;
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presents new methods including machine learning and compressive sampling, which help to improve safety, reliability, and performance.A;
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machine learning problems Key Features Delve into machine learning with this comprehensive guide to scikit-learn and scientific Python Master the;
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Machine Learning in Healthcare discusses how to build various ML algorithms and how they can be applied to improve healthcare systems. It;
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Vergelijkbare producten zoals Machine Learning with Python: The Ultimate Guide to Learn Machine Learning Algorithms. Includes a Useful Section about Analysis, Data Mining and Art
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