Book Image

Python Machine Learning Workbook for Beginners

By : AI Sciences
Book Image

Python Machine Learning Workbook for Beginners

By: AI Sciences

Overview of this book

<p>Machine Learning (ML) is the lifeblood of businesses worldwide. ML tools empower organizations to identify profitable opportunities fast and help them to better understand potential risks. The ever-expanding data, cost-effective data storage, and competitively priced powerful processing continue to drive the growth of ML. </p><p> </p><p>This is the best time you could enter the exciting machine learning universe. Industries are reinventing themselves constantly by developing more advanced data analysis models. These models analyze larger and more complex data than ever while delivering instantaneous and more accurate results on enormous scales. </p><p>In this backdrop, it is evident that hands-on practice is everything in machine learning. Tons of theory will amount to nothing if you don’t have enough hands-on practice. Textbooks and online classes mislead you into a false sense of mastery. The easy availability of learning resources tricks you and you become overconfident. But when you try to apply the theoretical concepts you have learned, you realize it’s not that simple. </p><p> </p><p>This is where projects play a crucial role in your learning journey. Projects are doubtless the best investment of your time. You’ll not only enjoy learning but you’ll also make quick progress. And unlike studying boring theoretical concepts, you’ll find that working on projects is easier to stay motivated. </p><p> </p><p>The projects in this book cover ten different interesting topics. Each project will help you refine your ML skills and apply them in the real world. These projects also present you with an opportunity to enrich your portfolio, making it simpler to find a great job, explore interesting career paths, and even negotiate a higher pay package. Overall, this learning-by-doing book will help you accomplish your machine learning career goals faster. </p><p> </p><p>The code bundle for this course is available at https://www.aispublishing.net/ai-sciences-book</p>
Table of Contents (15 chapters)
1
About the Author

PROJECT

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Filtering Spam Email Messages Using Naïve Bayes Algorithm

If you have used Gmail, Yahoo, or any other email service, you would have noticed that some emails are automatically marked as spam by email engines. These spam email detectors are based on rule-based and statistical machine learning approaches.

Spam email filtering is a text classification task, where based on the text of the email, we have to classify whether or not an email is a spam email. Supervised machine learning is commonly used for classification, particularly if the true outputs are available in the dataset.

The Naïve Bayes Algorithm is one of the supervised machine learning algorithms that have been proven to be effective for spam email detection. In this project, you will see how to detect spam emails using the Naïve Bayes algorithm implemented via Python’s Sklearn library.

Why Use Naïve Bayes Algorithm?

Linear regression algorithm is particularly useful when:

1.Performs brilliantly...