This document discusses text classification, which involves assigning categories or classes to text documents. It describes common applications like spam filtering and news categorization. Several machine learning models for text classification are covered, including naive bayes, decision trees, document vector models, K-nearest neighbors (KNN), support vector machines (SVM), logistic regression, and fastText. The concepts of overfitting and underfitting are also briefly discussed.

1. Hands on Machine-Learning.
Text Classification

2. What is Classification?
> Put objects like images, documents, data points etc into their proper
class.
> Automatically assign class to an unseen object.

3. Why Text-Classification?
> Spam filtering.
>Document Categorization (medical, scientific, entertainment etc)
> News Classification.
> Sentiment Analysis.
> Search query-category classification.

4. News Classification

5. Models
> Naive Bayes.
> Decision trees.
> Document Vector Model based.
> KNN
> SVM

6. Naive Bayes

7. Naive Bayes

8. Decision trees

9. Document Vectors

10. Naive Bayes

11. Naive Bayes

12. Naive Bayes

16. Linear Regression
Hyperplane
(decision
boundary)

17. Logistic Regression

19. SVM: Large margin classifiers.
Large Margin
boundary

20. Overfitting vs Underfitting
Underfitting
Overfitting

21. TF-IDF

22. Binary vs Multi-class(one vs rest)

23. FastText.

24. Show me the code?