{"product_id":"naive-bayes-classifiers-33-comprehensively-commented-python-implementations-of-naive-bayes-classifiers-9798307527634","title":"Naive Bayes Classifiers: 33 Comprehensively Commented Python Implementations of Naive Bayes Classifiers","description":"\u003cp\u003e • Author(s): Jamie Flux\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Data Science - Neural Networks\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eEmpower your data-driven decisions and scale your machine learning expertise\u003c\/b\u003e with this rigorous yet accessible resource on Naive Bayes classifiers. Designed for both academic research and professional deployment, this guide introduces you to the inner workings of Bayesian methods while providing \u003cb\u003e33 fully-coded solutions in Python\u003c\/b\u003e. It bridges the gap between theoretical underpinnings and real-world efficacy, ensuring you gain both \u003cb\u003epractical\u003c\/b\u003e and \u003cb\u003econceptual\u003c\/b\u003e mastery.\u003c\/p\u003e \u003cp\u003eWith meticulously explained code examples and in-depth algorithmic breakdowns, you will learn how to: \u003c\/p\u003e \u003cul\u003e\n\u003cli\u003e\n\u003cb\u003eEngineer spam email filters\u003c\/b\u003e using Multinomial Naive Bayes for robust text-based categorization.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eDetect fake news\u003c\/b\u003e by modeling linguistic patterns and content signals, supporting insightful media verification.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eClassify medical diagnoses\u003c\/b\u003e with Gaussian Naive Bayes for continuous data, bridging clinical insights and numerical features.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eOptimize customer churn prediction\u003c\/b\u003e and target retention strategies using straightforward Bernoulli Naive Bayes.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eTrack social media ideology\u003c\/b\u003e with text mining to differentiate political stances in user posts.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eImplement real-time tweet classification\u003c\/b\u003e for event detection, harnessing partial-fit methods to continuously update outcomes.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eIdentify anomalies in IoT sensor streams\u003c\/b\u003e to maintain operational stability and catch faults early.\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003eWhether you are a data scientist seeking to consolidate your machine learning toolset or a researcher exploring new avenues for predictive modeling, this book delivers \u003cb\u003eclear demonstrations\u003c\/b\u003e, \u003cb\u003ereusable scripts\u003c\/b\u003e, and \u003cb\u003eillustrative best practices\u003c\/b\u003e that will expedite your projects from inception to production.\u003c\/p\u003e\u003cbr\u003e","brand":"Independently Published","offers":[{"title":"Paperback","offer_id":45556688027799,"sku":"9798307527634","price":2759.0,"currency_code":"INR","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9798307527634.webp?v=1768592001","url":"https:\/\/atlanticbooks.com\/products\/naive-bayes-classifiers-33-comprehensively-commented-python-implementations-of-naive-bayes-classifiers-9798307527634","provider":"Atlantic Books","version":"1.0","type":"link"}