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DATA MINING and MACHINE LEARNING. CLASSIFICATION PREDICTIVE TECHNIQUES: SUPPORT VECTOR MACHINE, LOGISTIC REGRESSION, DISCRIMINANT ANALYSIS and DECISION TREES

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Machine Learning with Scala Quick Start Guide

techniques with best practices and pitfalls Learn classification and regression analysis with linear regression, logistic regression, Naive Bayes;

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Data Mining and Statistics for Decision Making

marketing objectives. This book looks at both classical and recent techniques of data mining, such as clustering, discriminant analysis, logistic;

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Mastering Machine Learning with R -

analysis * Master fundamental learning methods such as linear and logistic regression * Comprehend advanced learning methods such as support;

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Data Science Solutions with Python

presented is a binary classification model (logistic regression) and an ensemble model (Gradient Boosted Trees). The book introduces DL and an;

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Data-Driven Computational Neuroscience

trees, rule induction, artificial neural networks and support vector machines) and probabilistic models (discriminant analysis, logistic;

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Machine Learning in Bio-Signal Analysis and Diagnostic Imaging

Machine Learning in Bio-Signal Analysis and Diagnostic Imaging presents original research on the advanced analysis and classification;

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Machine Learning Pocket Reference Working with Structured Data in Python

learning process and walks you through classification with structured data. You'll also learn methods for clustering, predicting a continuous value;

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Machine Learning with PySpark

supervised machine learning models such as linear regression, logistic regression, decision trees, and random forest. You'll also see unsupervised;

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Artificial Intelligence in Civil Engineering

Inference System, Support Vector Machine, Least Square Support Vector Machine, Relevance Vector Machine, Multivariate Adaptive Regression Spline;

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Mathematical Analysis For Machine Learning And Data Mining

This compendium provides a self-contained introduction to mathematical analysis in the field of machine learning and data mining. The;

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Mastering Machine Learning with R

and training data sets for analysis * Familiarize yourself with fundamental learning methods such as linear and logistic regression;

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Training Systems Using Python Statistical Modeling

classification using k-nearest neighbors, decision trees, and random forests. This book also covers algorithms for regression analysis, such as ridge and;

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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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Knowledge Discovery with Support Vector Machines

Regression with support vector machines Novelty detection Complemented with hands-on exercises, algorithm descriptions, and data sets, Knowledge;

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Predictive Analytics

regression, logistic regression (binary and multinomial), discriminant analysis, Bayesian classification, generalized linear models and Cox;

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Mastering Machine Learning with scikit-learn -

K Nearest Neighbors * Classify documents and images using logistic regression and support vector machines * Create ensembles of estimators;

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MATLAB for Machine Learning

learning and you'll see how to easily interact with the Matlab workspace. We'll then move on to data cleansing, mining and analyzing various data;

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Machine Learning for OpenCV

effective use of support vector machines, boosted decision trees, and random forests * Get acquainted with neural networks and Deep Learning to;

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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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Data Science with Python

learning. You will also explore basic classification and regression techniques, such as support vector machines, decision trees, and logistic;

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Artificial Intelligence and Machine Learning Fundamentals

regression and classification concepts to real-world problems Perform predictive analysis using decision trees and random forests Carry out;

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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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Predictive Analytics and Data Mining

> Predictive analytics and Data Mining techniques covered: Exploratory Data Analysis, Visualization, Decision trees, Rule induction, k-Nearest;

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Data Science Algorithms in a Week

neighbors, Naive Bayes, decision trees, random forest, k-means, regression, and time-series analysis. By the end of this book, you will understand;

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Data Science

, Machine Learning, and Big Data - That You Don't Data Science for Business: Predictive Modeling, Data Mining, Data Analytics, Data Warehousing;

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