Naive Bayes Classifiers: 33 Comprehensively Commented Python Implementations of Naive Bayes Classifiers (Stochastic Sorcerers) - Softcover
Book 4 of 10: Stochastic Sorcerers
Synopsis
Empower your data-driven decisions and scale your machine learning expertise 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 33 fully-coded solutions in Python. It bridges the gap between theoretical underpinnings and real-world efficacy, ensuring you gain both practical and conceptual mastery.
With meticulously explained code examples and in-depth algorithmic breakdowns, you will learn how to:
- Engineer spam email filters using Multinomial Naive Bayes for robust text-based categorization.
- Detect fake news by modeling linguistic patterns and content signals, supporting insightful media verification.
- Classify medical diagnoses with Gaussian Naive Bayes for continuous data, bridging clinical insights and numerical features.
- Optimize customer churn prediction and target retention strategies using straightforward Bernoulli Naive Bayes.
- Track social media ideology with text mining to differentiate political stances in user posts.
- Implement real-time tweet classification for event detection, harnessing partial-fit methods to continuously update outcomes.
- Identify anomalies in IoT sensor streams to maintain operational stability and catch faults early.
Whether you are a data scientist seeking to consolidate your machine learning toolset or a researcher exploring new avenues for predictive modeling, this book delivers clear demonstrations, reusable scripts, and illustrative best practices that will expedite your projects from inception to production.
"synopsis" may belong to another edition of this title.
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Paperback. Condition: new. Paperback. Empower your data-driven decisions and scale your machine learning expertise 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 33 fully-coded solutions in Python. It bridges the gap between theoretical underpinnings and real-world efficacy, ensuring you gain both practical and conceptual mastery. With meticulously explained code examples and in-depth algorithmic breakdowns, you will learn how to: Engineer spam email filters using Multinomial Naive Bayes for robust text-based categorization.Detect fake news by modeling linguistic patterns and content signals, supporting insightful media verification.Classify medical diagnoses with Gaussian Naive Bayes for continuous data, bridging clinical insights and numerical features.Optimize customer churn prediction and target retention strategies using straightforward Bernoulli Naive Bayes.Track social media ideology with text mining to differentiate political stances in user posts.Implement real-time tweet classification for event detection, harnessing partial-fit methods to continuously update outcomes.Identify anomalies in IoT sensor streams to maintain operational stability and catch faults early. Whether you are a data scientist seeking to consolidate your machine learning toolset or a researcher exploring new avenues for predictive modeling, this book delivers clear demonstrations, reusable scripts, and illustrative best practices that will expedite your projects from inception to production. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9798307527634
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Naive Bayes Classifiers (Paperback)
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Paperback. Condition: new. Paperback. Empower your data-driven decisions and scale your machine learning expertise 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 33 fully-coded solutions in Python. It bridges the gap between theoretical underpinnings and real-world efficacy, ensuring you gain both practical and conceptual mastery. With meticulously explained code examples and in-depth algorithmic breakdowns, you will learn how to: Engineer spam email filters using Multinomial Naive Bayes for robust text-based categorization.Detect fake news by modeling linguistic patterns and content signals, supporting insightful media verification.Classify medical diagnoses with Gaussian Naive Bayes for continuous data, bridging clinical insights and numerical features.Optimize customer churn prediction and target retention strategies using straightforward Bernoulli Naive Bayes.Track social media ideology with text mining to differentiate political stances in user posts.Implement real-time tweet classification for event detection, harnessing partial-fit methods to continuously update outcomes.Identify anomalies in IoT sensor streams to maintain operational stability and catch faults early. Whether you are a data scientist seeking to consolidate your machine learning toolset or a researcher exploring new avenues for predictive modeling, this book delivers clear demonstrations, reusable scripts, and illustrative best practices that will expedite your projects from inception to production. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9798307527634
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Taschenbuch. Condition: Neu. Neuware - Empower your data-driven decisions and scale your machine learning expertise 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 33 fully-coded solutions in Python. It bridges the gap between theoretical underpinnings and real-world efficacy, ensuring you gain both practical and conceptual mastery. Seller Inventory # 9798307527634