Brand New! Not Overstocks or Low Quality Book Club Editions! Direct From the Publisher! We're not a giant, faceless warehouse organization! We're a small town bookstore that loves books and loves it's customers! Buy from Lakeside Books!

N� de ref. del art�culo OTF-S-9781492035640

Contactar al vendedor

Denunciar este art�culo

Detalles bibliogr�ficos

T�tulo

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

Editor

O'Reilly Media

Fecha de publicaci�n

2019

Idioma

Ingl�s

ISBN 10

1492035645

ISBN 13

9781492035640

Encuadernaci�n

Encuadernaci�n de tapa blanda

Estado

New

Cat�logos de vendedores

Acerca de

Sinopsis

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

Acerca del autor

Ankur Patel is an applied machine learning researcher and data scientist with expertise in financial markets. His work focuses on unsupervised learning, natural language processing, time series prediction, and sequential data problems. Currently, Ankur finds hidden patterns in large-scale unlabeled data for clients around the world as a data scientist at ThetaRay, an Israeli artificial intelligence firm. Ankur started his career as the lead emerging markets trader at Bridgewater Associates and later founded and managed the machine learning-based hedge fund R-Squared Macro.