Nouveauté
Machine Learning through Python. Unsupervised Learning: Dimension Reduction, Segmentation, and Neural Networks. MACHINE LEARNING
Par :Formats :
Disponible dans votre compte client Decitre ou Furet du Nord dès validation de votre commande. Le format ePub est :
- Compatible avec une lecture sur My Vivlio (smartphone, tablette, ordinateur)
- Compatible avec une lecture sur liseuses Vivlio
- Pour les liseuses autres que Vivlio, vous devez utiliser le logiciel Adobe Digital Edition. Non compatible avec la lecture sur les liseuses Kindle, Remarkable et Sony
, qui est-ce ?Notre partenaire de plateforme de lecture numérique où vous retrouverez l'ensemble de vos ebooks gratuitement
Pour en savoir plus sur nos ebooks, consultez notre aide en ligne ici
C'est si simple ! Lisez votre ebook avec l'app Vivlio sur votre tablette, mobile ou ordinateur :
- FormatePub
- ISBN8232823849
- EAN9798232823849
- Date de parution10/11/2025
- Protection num.pas de protection
- Infos supplémentairesepub
- ÉditeurHamza elmir
Résumé
Machine learning is an interdisciplinary field that uses methods, algorithms, processes, and systems to extract knowledge and conclusions from structured and unstructured data. It combines elements of statistics, computer science, mathematics, and analytical techniques to solve problems, make predictions, and generate value from data. It relies on big data to discover patterns, trends, and relationships that can be used for decision-making in various industries.
It is an important support for Artificial Intelligence. Machine learning uses two types of techniques: supervised learning, which trains a model with known input and output data to predict future outcomes, and unsupervised learning, which finds hidden patterns or intrinsic structures in the input data. Most of these unsupervised learning techniques are developed throughout this book from a methodological and practical perspective with applications through the Python software.
The following techniques are covered: dimension reduction, principal components analysis, factor analysis, simple correspondence analysis, multiple correspondence analysis, multidimensional scaling, neural networks (SOM Kohonen, etc.), pattern recognition, anomaly detection, autoencoders, image processing, and convolutional neural networks (CNNs).
It is an important support for Artificial Intelligence. Machine learning uses two types of techniques: supervised learning, which trains a model with known input and output data to predict future outcomes, and unsupervised learning, which finds hidden patterns or intrinsic structures in the input data. Most of these unsupervised learning techniques are developed throughout this book from a methodological and practical perspective with applications through the Python software.
The following techniques are covered: dimension reduction, principal components analysis, factor analysis, simple correspondence analysis, multiple correspondence analysis, multidimensional scaling, neural networks (SOM Kohonen, etc.), pattern recognition, anomaly detection, autoencoders, image processing, and convolutional neural networks (CNNs).
Machine learning is an interdisciplinary field that uses methods, algorithms, processes, and systems to extract knowledge and conclusions from structured and unstructured data. It combines elements of statistics, computer science, mathematics, and analytical techniques to solve problems, make predictions, and generate value from data. It relies on big data to discover patterns, trends, and relationships that can be used for decision-making in various industries.
It is an important support for Artificial Intelligence. Machine learning uses two types of techniques: supervised learning, which trains a model with known input and output data to predict future outcomes, and unsupervised learning, which finds hidden patterns or intrinsic structures in the input data. Most of these unsupervised learning techniques are developed throughout this book from a methodological and practical perspective with applications through the Python software.
The following techniques are covered: dimension reduction, principal components analysis, factor analysis, simple correspondence analysis, multiple correspondence analysis, multidimensional scaling, neural networks (SOM Kohonen, etc.), pattern recognition, anomaly detection, autoencoders, image processing, and convolutional neural networks (CNNs).
It is an important support for Artificial Intelligence. Machine learning uses two types of techniques: supervised learning, which trains a model with known input and output data to predict future outcomes, and unsupervised learning, which finds hidden patterns or intrinsic structures in the input data. Most of these unsupervised learning techniques are developed throughout this book from a methodological and practical perspective with applications through the Python software.
The following techniques are covered: dimension reduction, principal components analysis, factor analysis, simple correspondence analysis, multiple correspondence analysis, multidimensional scaling, neural networks (SOM Kohonen, etc.), pattern recognition, anomaly detection, autoencoders, image processing, and convolutional neural networks (CNNs).
