Hands-On Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data - Marjane Mall - Image 1
220
00DH
300.00 DH
-27%

Hands-On Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data

Livraison

Détails
Frais de livraison à partir de :
Livraison entre le Lundi 29 juin 2026 et le Mardi 30 juin 2026

À propos de cet article :

Marque : GENERIC
Vendu par KECHBOOK

Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied. Unsupervised learning, on the other hand, can be applied to unlabeled datasets to di...

1

Mode de paiement

Paiement par carte bancaire
Carte marocaines
Paiement à la livraison
Paiement en espèce à la livraison
Politique de retours
Note de politique de retour

Description produit

Marque
GENERIC
Titre principal
Hands-On Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data
Editeur
O'Reilly Media, Inc, USA
Type de produit
paperback
Présentation du livre
paperback
Release date
1/1/2019 12:00:00 AM
Langue d'origine
English
ISBN
1492035645
Dimensions
17.78 x 1.91 x 22.86 cm
Nombre de pages de livre
359 pages
Langue - Librairie
English
Résumé
Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied. Unsupervised learning, on the other hand, can be applied to unlabeled datasets to discover meaningful patterns buried deep in the data, patterns that may be near impossible for humans to uncover. Author Ankur Patel shows you how to apply unsupervised learning using two simple, production-ready Python frameworks: Scikit-learn and TensorFlow using Keras. With code and hands-on examples, data scientists will identify difficult-to-find patterns in data and gain deeper business insight, detect anomalies, perform automatic feature engineering and selection, and generate synthetic datasets. All you need is programming and some machine learning experience to get started. Compare the strengths and weaknesses of the different machine learning approaches: supervised, unsupervised, and reinforcement learning Set up and manage machine learning projects end-to-end Build an anomaly detection system to catch credit card fraud Clusters users into distinct and homogeneous groups Perform semisupervised learning Develop movie recommender systems using restricted Boltzmann machines Generate synthetic images using generative adversarial networks Read more
Auteur(s)
Ankur A Patel
Date de parution
1/1/2019 12:00:00 AM