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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 Foundations

techniques. The book consists of four parts: foundation, supervised learning, unsupervised learning, and advanced learning. The first part provides;

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TensorFlow 2.0 Quick Start Guide

Perform supervised and unsupervised machine learning and learn advanced techniques such as training neural networks. Key Features Train;

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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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Machine Learning with scikit-learn Quick Start Guide

. Key Features Build your first machine learning model using scikit-learn Train supervised and unsupervised models using popular techniques such;

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Human Action Analysis with Randomized Trees

trees and the unsupervised random trees. When there are sufficient amount of labeled data available, supervised random trees provides a fast;

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Semi-Supervised and Unsupervised Machine Learning

research topic associated to unsupervised techniques with multiple applications and challenges, from web-content mining to the inference of cancer;

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Hands-On Unsupervised Learning with Python

. This book starts with the key differences between supervised, unsupervised, and semi-supervised learning. You will be introduced to the best;

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Introduction to Semi-Supervised Learning

, outlier detection) where all the data are unlabeled, or in the supervised paradigm (e.g., classification, regression) where all the data are;

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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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Supervised Descriptive Pattern Mining

term supervised descriptive pattern mining. It considers the analysis of different algorithms either based on heuristics or based on exhaustive;

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Supervised and Unsupervised Pattern Recognition

and computational intelligence, as Supervised and Unsupervised Pattern Recognition does. This volume describes the application of a novel;

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Python Machine Learning Cookbook

to supervised, unsupervised and reinforcement learning techniques Find easy-to-follow code solutions for tackling common and not-so-common;

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Machine Learning with Scala Quick Start Guide

covers tree-based ensemble techniques for solving both classification and regression problems. Moving ahead, it covers unsupervised learning;

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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 Fundamentals

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

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Machine Learning for Knowledge Discovery with R

: Contains statistical theory for the most recent supervised and unsupervised machine learning methodologies. Emphasizes broad statistical thinking;

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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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Automated Data Collection With R

supervised and unsupervised techniques as well as advanced techniques such as data scraping and text management. * Case studies are featured;

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Remote Sensing

needed for attitude control; and techniques to study the urban growth over time, combining unsupervised, supervised classifications and the;

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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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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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Mastering Machine Learning Algorithms

learning, supervised learning, and unsupervised learning domains. You will use all the modern libraries from the Python ecosystem - including NumPy;

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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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Mathematical Foundations for Data Analysis

This textbook, suitable for an early undergraduate up to a graduate course, provides an overview of many basic principles and techniques;

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Mathematical Foundations for Data Analysis

This textbook, suitable for an early undergraduate up to a graduate course, provides an overview of many basic principles and techniques;

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