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

Ankur A Patel

ISBN 10: 1492035645 / ISBN 13: 9781492035640

Editore: O'reilly Media Apr 2019, 2019

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Neuware - Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to the holy grail in AI research, the so-called general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied; this is where unsupervised learning comes in. Unsupervised learning 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 provides practical knowledge on how to apply unsupervised learning using two simple, production-ready Python frameworks - scikit-learn and TensorFlow using Keras. With the hands-on examples and code provided, you 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.

Codice articolo 9781492035640

Dati bibliografici

• Titolo: Hands-On Unsupervised Learning Using Python : How to Build Applied Machine Learning Solutions from Unlabeled Data
• Editore: O'reilly Media Apr 2019
• Data di pubblicazione: 2019
• Lingua: Inglese
• ISBN 10: 1492035645
• ISBN 13: 9781492035640
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 233 L x 183 A x 22 P (millimetri) 233 millimetri di larghezza per 183 millimetri di altezza per 22 millimetri di profondità
• Peso: 640 grammi

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Riassunto

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

Informazioni sull?autore

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.