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Learning Algorithms to cluster unlabelled data into groups Perform classification and regression machine learning Use an effective pipeline to;
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This book presents machine learning models and algorithms to address big data classification problems. Existing machine learning techniques;
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areas as pattern recognition, database, data mining, and machine learning. Addressing the work of these different communities in a unified way;
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areas as pattern recognition, database, data mining, and machine learning. Addressing the work of these different communities in a unified way;
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embedding for classification. After reading this book, you will understand how to use PySpark's machine learning library to build and train;
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models using individual recipes. What you will learn Understand how to use machine learning algorithms for regression and classification problems;
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algorithms and applications, feature extraction and classification, theories and models for plausible reasoning, computational learning theory;
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help you understand different aspects of machine learning. You will see how to pre-cluster your data to optimize and classify it for large;
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details multiple types of data generating from urban activities and suitability of various machine learning algorithms for handling urban data;
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for students, researchers, and practitioners. The book lays the foundations of data analysis, pattern mining, clustering, classification and;
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concepts that make it easy to build scalable and complex big data applications. This book is a handy guide for machine learning developers and data;
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of this book is to provide awareness of algorithms used for machine learning and big data in the academic and professional community. The 17;
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learning algorithms for supervised and unsupervised cases Examine statistical concepts for designing data for use in models Dive into linear;
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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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data for analysis in Azure Machine Learning and export from Azure Machine Learning Develop machine learning models Operationalize and;
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Introduction to Algorithms for Data Mining and Machine Learning introduces the essential ideas behind all key algorithms and techniques;
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a data analyst with a fundamental knowledge of Scala who wants to learn and implement various Machine learning techniques, this book is for;
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Supplement methods with statistical tests, such as ROC Walk through data structures in classification, regression, survival, and time series data Use;
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-based practical guide * Work with important classification and regression algorithms and other machine learning techniques Who This Book;
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latest in deep learning through CNNs, RNNs, and word2vec for text mining. You will be learning not only the algorithms, but also the concepts;
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of your data. This book makes machine learning with C++ for beginners easy with its example-based approach, demonstrating how to implement;
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; classification algorithms in handling noisy training data and meta data generation for automates web page classification.;
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prediction Who This Book Is For This book caters to aspiring data scientists who are well versed with machine learning concepts with R and are;
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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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implement these algorithms. You'll discover how to deal with various types of data and explore the differences between machine learning paradigms;
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), Bayesian Networks (BN) and deep learning. Further, numerous data mining techniques are discussed, as they have proven to be good classifiers for;
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Reinforcement Learning techniques Build end-to-end deep learning (CNN, RNN, and Autoencoders) models Who this book is forThis Learning Path is for data;
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