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Applied Supervised Learning with R

Learn the ropes of supervised machine learning with R by studying popular real-world use-cases, and understand how it drives object;

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Applied Supervised Learning with Python

input data-has revolutionized the way we do business. Applied Supervised Learning with Python provides a rich understanding of how you can apply;

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Introduction to Semi-Supervised Learning

Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the;

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Behavior Analysis with Machine Learning Using R

Behavior Analysis with Machine Learning Using R introduces machine learning and deep learning concepts and algorithms applied to a diverse;

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Welding and Cutting Case Studies with Supervised Machine Learning

learning. Subsequently, it discusses supervised learning, highlighting Python libraries such as NumPy, Pandas and Scikit Learn programming. It also;

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Welding and Cutting Case Studies with Supervised Machine Learning

learning. Subsequently, it discusses supervised learning, highlighting Python libraries such as NumPy, Pandas and Scikit Learn programming. It also;

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Machine Learning with scikit-learn Quick Start Guide

learning environment with scikit-learn. To put scikit-learn to use, you will learn how to implement various supervised and unsupervised machine;

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The The Supervised Learning Workshop

Cut through the noise and get real results with a step-by-step approach to understanding supervised learning algorithms Key Features;

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Supervised Machine Learning for Text Analysis in R

workflows for predictive modeling using text as features Compare traditional machine learning methods and deep learning methods for text data;

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Supervised Machine Learning for Text Analysis in R

workflows for predictive modeling using text as features Compare traditional machine learning methods and deep learning methods for text data;

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Machine Learning for Knowledge Discovery with R

Machine Learning for Knowledge Discovery with R contains methodologies and examples for statistical modelling, inference, and prediction;

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Supervised and Unsupervised Learning for Data Science

This book covers the state of the art in learning algorithms with an inclusion of semi-supervised methods to provide a broad scope;

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Applied Machine Learning for Smart Data Analysis

unsupervised and semi-supervised machine learning techniques with study of semantic analysis and thorough analysis of reviews. Divided into sections;

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Applied Machine Learning

. Coverage includes: *Supervised learning*Statistical learning*Learning with support vector machines (SVM)*Learning with neural networks (NN)*Fuzzy;

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Machine Learning Foundations

This book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning;

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Supervised Reinforcement Learning

Supervised Reinforcement Learning is een boek van Karla Conn;

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Supervised Machine Learning

interoperability with R libraries to accomplish variety of statistical and machine-learning tasks Computer programs in R and SAS that create AI framework;

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Supervised Machine Learning

interoperability with R libraries to accomplish variety of statistical and machine-learning tasks Computer programs in R and SAS that create AI framework;

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HandsOn Unsupervised Learning Using Python

artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied. Unsupervised;

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Beginning Anomaly Detection Using Python-Based Deep Learning

and PyTorch in Python, the book focuses on how various deep learning models can be applied to semi-supervised and unsupervised anomaly;

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Machine Learning and Data Science

techniques required to excel with statistical learning methods in the areas of data access, data munging, exploratory data analysis, supervised;

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Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction

supervised Machine Learning. The specific topics of ramp event prediction and wake interactions are addressed in this book, along with forecasted;

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Supervised Machine Learning with Python

a deep understanding along the way. You'll embark on this journey with a quick overview and see how supervised machine learning differs from;

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Machine Learning in Complex Networks

in details some specific techniques based on complex networks for supervised, non-supervised, and semi-supervised learning. Particularly, a;

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Machine Learning in Complex Networks

in details some specific techniques based on complex networks for supervised, non-supervised, and semi-supervised learning. Particularly, a;

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Machine Learning and Knowledge Discovery in Databases: European Conference, Ecml Pkdd 2019, Würzburg, Germany, September 16-20, 2019, Proceedings, Par

security; optimization. Part II: supervised learning; multi-label learning; large-scale learning; deep learning; probabilistic models;

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Introduction to Machine Learning with R

guide will help you gain a solid foundation in machine learning principles. Using the R programming language, you'll first start to learn with;

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