{"product_id":"hands-on-unsupervised-learning-using-python-how-to-build-applied-machine-learning-solutions-from-unlabeled-data-paperback","title":"Hands-On Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data - Paperback","description":"\u003cp\u003eby \u003cb\u003eAnkur Patel\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eMany 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. \u003c\/p\u003e\u003cp\u003e 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. \u003c\/p\u003e\u003cul\u003e \u003cli\u003eCompare the strengths and weaknesses of the different machine learning approaches: supervised, unsupervised, and reinforcement learning \u003c\/li\u003e\n\u003cli\u003eSet up and manage machine learning projects end-to-end \u003c\/li\u003e\n\u003cli\u003eBuild an anomaly detection system to catch credit card fraud \u003c\/li\u003e\n\u003cli\u003eClusters users into distinct and homogeneous groups \u003c\/li\u003e\n\u003cli\u003ePerform semisupervised learning \u003c\/li\u003e\n\u003cli\u003eDevelop movie recommender systems using restricted Boltzmann machines \u003c\/li\u003e\n\u003cli\u003eGenerate synthetic images using generative adversarial networks \u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eAnkur 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.\u003c\/p\u003e\u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 359\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.7 x 9.1 x 7 IN\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e April 16, 2019\u003c\/div\u003e","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":51760664346912,"sku":"9781492035640","price":79.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0974\/9764\/5344\/files\/dbc7829233c097a80c8219878a8ab69c.webp?v=1780186202","url":"https:\/\/ebocreations.com\/products\/hands-on-unsupervised-learning-using-python-how-to-build-applied-machine-learning-solutions-from-unlabeled-data-paperback","provider":"The E-Book Oasis LLC","version":"1.0","type":"link"}