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Study mode:
on
1
Intro
2
About Lorena
3
Todays questions
4
What is machine learning
5
Defining machine learning
6
Learning from experience
7
Learning from pain
8
Learning from memories
9
What does experience mean
10
Naive Bayes
11
Bayes Theorem
12
Assumptions
13
Bayesian classifiers
14
Why naive Bayes
15
How to detect spam
16
What are we going to use
17
Bag of Words
18
Classification
19
Performance Measurement
20
False Positives
21
Side Effects
22
Improving Performance
23
Recommended Resources
24
Conclusion
Description:
Explore a beginner-friendly introduction to machine learning and spam detection in this EuroPython conference talk. Learn key concepts of supervised learning and classifiers as you build a basic email spam filter using Python and the Naive Bayes algorithm. Discover how to define machine learning problems, understand the Bayesian approach, and implement a "bag of words" model for text classification. Gain insights into performance measurement, false positives, and ways to improve your spam detection model. Walk through the process of training a classifier using labeled examples and applying it to new data. Perfect for those new to programming or Python who want to demystify machine learning concepts and gain practical experience in building a common type of classifier.

Is That Spam in My Ham?

EuroPython Conference
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