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Hands-On Unsupervised Learning Using Python - How to Build Applied Machine Learning Solutions from Unlabeled Data (Paperback) Loot Price: R1,318
Discovery Miles 13 180
You Save: R344 (21%)
Hands-On Unsupervised Learning Using Python - How to Build Applied Machine Learning Solutions from Unlabeled Data (Paperback):...

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

Ankur A. Patel

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List price R1,662 Loot Price R1,318 Discovery Miles 13 180 | Repayment Terms: R124 pm x 12* You Save R344 (21%)

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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

General

Imprint: O'Reilly Media
Country of origin: United States
Release date: March 2019
Authors: Ankur A. Patel
Dimensions: 250 x 150 x 15mm (L x W x T)
Format: Paperback
Pages: 400
ISBN-13: 978-1-4920-3564-0
Categories: Books
LSN: 1-4920-3564-5
Barcode: 9781492035640

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