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Supervised and Unsupervised Learning for Data Science

in presenting current approaches to unsupervised and semi-supervised learning in graduate-level seminar courses. The book is based on selected, expanded;

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Fundamentals of Data Analytics

data science is used for classification as well as supervised and unsupervised learning.;

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Fundamentals of Data Analytics

data science is used for classification as well as supervised and unsupervised learning.;

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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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Statistical Learning and Data Science

analysis, more and more complex data have become available for processing. Supervised machine learning, semi-supervised analysis approaches, and;

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Statistical Learning and Data Science

analysis, more and more complex data have become available for processing. Supervised machine learning, semi-supervised analysis approaches, and;

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Sampling Techniques for Supervised or Unsupervised Tasks

This book describes in detail sampling techniques that can be used for unsupervised and supervised cases, with a focus on sampling;

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Sampling Techniques for Supervised or Unsupervised Tasks

This book describes in detail sampling techniques that can be used for unsupervised and supervised cases, with a focus on sampling;

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Machine Learning for Knowledge Discovery with R

of data analysis. It includes many recent supervised and unsupervised machine learning methodologies such as recursive partitioning modelling;

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Multimodal Analytics for Next-Generation Big Data Technologies and Applications

data; unsupervised learning strategies for big multimodal data; supervised learning strategies for big multimodal data; and multimodal big data;

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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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Applied Machine Learning for Smart Data Analysis

techniques namely unsupervised and semi-supervised for unseen and seen data sets Case studies are covered relating to human health, transportation;

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Hands-On Unsupervised Learning with Python

the concept of unsupervised learning to cluster large sets of data and analyze them repeatedly until the desired outcome is found using Python;

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Machine Learning with scikit-learn Quick Start Guide

DescriptionScikit-learn is a robust machine learning library for the Python programming language. It provides a set of supervised and unsupervised learning;

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Introduction to Semi-Supervised Learning

presence of both labeled and unlabeled data. Traditionally, learning has been studied either in the unsupervised paradigm (e.g., clustering;

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Semi-Supervised and Unsupervised Machine Learning

This book provides a detailed and up-to-date overview on classification and data mining methods. The first part is focused on supervised;

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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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Data Science Solutions with Python

Apply supervised and unsupervised learning to solve practical and real-world big data problems. This book teaches you how to engineer;

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Machine Learning Fundamentals

Understand the difference between supervised and unsupervised models Learn the usage of machine learning through real-world examples Book;

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Mastering Machine Learning Algorithms

data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement;

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Machine Learning and Data Science

machine learning, unsupervised machine learning and model evaluation. Machine learning and data science are large disciplines, requiring years;

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Robust Representation for Data Analytics

unsupervised learning, supervised learning, semi-supervised learning, multi-view learning, transfer learning, and deep learning. Robust Representations;

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Machine Learning and Data Science Blueprints for Finance From Building Trading Strategies to RoboAdvisors Using Python

over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP). Ideal for;

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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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Data Science with Java

supervised and unsupervised learning algorithms, and methods for evaluating their success Get up and running with MapReduce, using customized;

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Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits

to use supervised, unsupervised, or reinforcement learning algorithms Find out how to collect and prepare your data for machine learning;

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TensorFlow 2.0 Quick Start Guide

your own models for effective prediction, using high-level Keras API Perform supervised and unsupervised machine learning and learn advanced;

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