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Machine Learning with scikit-learn Quick Start Guide

Learning Algorithms to cluster unlabelled data into groups Perform classification and regression machine learning Use an effective pipeline to;

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Machine Learning Models and Algorithms for Big Data Classification

This book presents machine learning models and algorithms to address big data classification problems. Existing machine learning techniques;

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Data Classification

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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Data Classification

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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Machine Learning with PySpark

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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Ensemble Machine Learning Cookbook

models using individual recipes. What you will learn Understand how to use machine learning algorithms for regression and classification problems;

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Machine Learning Theoretical Foundations and Practical Applications

algorithms and applications, feature extraction and classification, theories and models for plausible reasoning, computational learning theory;

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Data Science Algorithms in a Week

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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Machine Learning Approaches for Urban Computing

details multiple types of data generating from urban activities and suitability of various machine learning algorithms for handling urban data;

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Data Mining and Machine Learning

for students, researchers, and practitioners. The book lays the foundations of data analysis, pattern mining, clustering, classification and;

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Machine Learning with Scala Quick Start Guide

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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Machine Learning and Big Data

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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Introduction to Machine Learning with R

learning algorithms for supervised and unsupervised cases Examine statistical concepts for designing data for use in models Dive into linear;

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Machine Learning Using R

Examine the latest technological advancements in building a scalable machine-learning model with big data using R. This second edition;

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Exam Ref 70-774 Perform Cloud Data Science with Azure Machine Learning

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

Introduction to Algorithms for Data Mining and Machine Learning introduces the essential ideas behind all key algorithms and techniques;

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Scala for Machine Learning -

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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Hands-On Ensemble Learning with R

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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Python Machine Learning By Example

-based practical guide * Work with important classification and regression algorithms and other machine learning techniques Who This Book;

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Pro Machine Learning Algorithms

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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Hands-On Machine Learning with C++

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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Genetic Algorithms, Fuzzy Systems & Website Classification

; classification algorithms in handling noisy training data and meta data generation for automates web page classification.;

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R Deep Learning Essentials

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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Machine Learning with SAP

Work smarter with machine learning! Begin with core machine learning concepts-types of learning, algorithms, data preparation, and more;

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Python Machine Learning Cookbook

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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Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis

), 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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Einde inhoud

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