{"product_id":"machine-learning-with-python-cookbook-practical-solutions-from-preprocessing-to-deep-learning-paperback","title":"Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eKyle Gallatin\u003c\/b\u003e (Author), \u003cb\u003eChris Albon\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThis practical guide provides more than 200 self-contained recipes to help you solve machine learning challenges you may encounter in your work. If you're comfortable with Python and its libraries, including pandas and scikit-learn, you'll be able to address specific problems, from loading data to training models and leveraging neural networks. \u003c\/p\u003e\u003cp\u003eEach recipe in this updated edition includes code that you can copy, paste, and run with a toy dataset to ensure that it works. From there, you can adapt these recipes according to your use case or application. Recipes include a discussion that explains the solution and provides meaningful context. \u003c\/p\u003e\u003cp\u003eGo beyond theory and concepts by learning the nuts and bolts you need to construct working machine learning applications. You'll find recipes for: \u003c\/p\u003e\u003cul\u003e \u003cli\u003eVectors, matrices, and arrays \u003c\/li\u003e\n\u003cli\u003eWorking with data from CSV, JSON, SQL, databases, cloud storage, and other sources \u003c\/li\u003e\n\u003cli\u003eHandling numerical and categorical data, text, images, and dates and times \u003c\/li\u003e\n\u003cli\u003eDimensionality reduction using feature extraction or feature selection \u003c\/li\u003e\n\u003cli\u003eModel evaluation and selection \u003c\/li\u003e\n\u003cli\u003eLinear and logical regression, trees and forests, and k-nearest neighbors \u003c\/li\u003e\n\u003cli\u003eSupporting vector machines (SVM), na舸e Bayes, clustering, and tree-based models \u003c\/li\u003e\n\u003cli\u003eSaving, loading, and serving trained models from multiple frameworks\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 413\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.85 x 9.19 x 7 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eIllustrated:\u003c\/strong\u003e Yes\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e September 05, 2023\u003c\/div\u003e\n            \u003c\/li\u003e\n\u003c\/ul\u003e","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":47213398753529,"sku":"9781098135720","price":86.38,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0789\/2782\/3097\/files\/8O3K5RmvnZ9781098135720.webp?v=1768104740","url":"https:\/\/bookscloud.io\/products\/machine-learning-with-python-cookbook-practical-solutions-from-preprocessing-to-deep-learning-paperback","provider":"BooksCloud Book Dropshipping","version":"1.0","type":"link"}