Linear SVM --Case Study Part 1

1. Linear SVM: A Case Study, First
Part
( Coding And Theory)
ANANDA SWARUP DAS, TECHNICAL STAFF MEMBER,
KNOWLEDGE ENGINEERING AND ENCRYPTED ANALYTICS TEAM,
IBM INDIA RESEARCH LABS, NEW DELHI.

2. Spam and Ham
How I read the data: Using Pandas
How My Data Looks:

3. Processing the Text
Number of Classes in My Data
Working with Text Data

4. TF-IDF (Term Frequency-Inverse Document
Frequency Vectors)
Output

5. TfidfTranformer and Our Purpose
Objective:
TfidfTransformer:

6. Hyperplane Definition (Theory for Linear SVM Begins)

7. Role of the bias

8. The hyperplane that we want
A Criteria for Linear SVM to work

9. Linear SVM and the Decision Rules

10. Orthogonality of the Weight Vector

14. The Concept of Margin

15. Why Margin is Expressed in terms of the unit
vector

16. Scaling the Margin