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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 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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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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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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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 with scikit-learn Quick Start Guide

Deploy supervised and unsupervised machine learning algorithms using scikit-learn to perform classification, regression, and clustering;

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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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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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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 Fundamentals

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

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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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Scaling up Machine Learning

, concurrent programming frameworks including CUDA, MPI, MapReduce and DryadLINQ, and learning settings (supervised, unsupervised, semi-supervised and;

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Scaling up Machine Learning

, concurrent programming frameworks including CUDA, MPI, MapReduce and DryadLINQ, and learning settings (supervised, unsupervised, semi-supervised and;

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Mastering Machine Learning Algorithms

machine learning algorithms and understand how they work in depth. One-stop solution to mastering supervised, unsupervised, and semi-supervised;

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Python Advanced Guide to Artificial Intelligence: Advanced Guide to Artificial Intelligence: Expert machine learning systems and intelligent agents us

supervised, unsupervised, and semi-supervised ML algorithms and their implementation Build deep learning models for object detection, image;

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HandsOn Unsupervised Learning Using Python

different machine learning approaches: supervised, unsupervised, and reinforcement learning Set up and manage machine learning projects end-to-end;

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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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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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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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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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Semi-Supervised Learning

Semi-supervised learning is an important area of machine learning. It deals with problems that involve a lot of unlabeled data and very;

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Machine Learning and Big Data

learning with various examples. An empirical study of supervised learning algorithms like Naive Bayes, KNN and semi-supervised learning algorithms;

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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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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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Hands-on Machine Learning with Python

visualizations. The second section covers machine learning basics and Scikit-learn library. It also explains supervised learning, unsupervised learning;

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