Natural Language Processing Algorithm...

Sep 20, 20240 likes30 views

Jyoti Yadav

Algorithm on NLP

Classical Vs Deep Learning Models
1. If – Else Rules (Chatbot)
2. Audio Frequency Components Analysis (Speech Recognition)
3. Bag-of-Words Model (Classification)
4. CNN for Text Recognition (Classification)
5. Sequence to Sequence (Many Applications)

Natural Language Processing-steps
1. Importing the Libraries
2. Importing the Dataset
3. Cleaning the Texts
4. Creating the Bag-of-words Model
5. Splitting the Dataset into Training and Test Sets
6. Training the Naïve Bayes Model on the Training Set
7. Predicting the Test Set Result
8. Making the Confusion Matrix.

Predicting if a single review is positive or negative
Use the model to predict if the following review:
"I love this restaurant so much“ is positive or negative.
Positive Review
Negative Review
Use the model to predict if the following review:
"I hate this restaurant so much“ is positive or negative.

Predicting if a single review is positive or negative

Natural Language Processing Algorithm...

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Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good. Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data. To learn more about our mission or provide feedback, take a look at www.deltanalytics.org. If you would like to use this material to further our mission of improving access to machine learning. Education please reach out to [email protected] .

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Natural Language Processing Algorithm...

1. Natural Language Processing 1

2. Types of NLP

3. Classical Vs Deep Learning Models

4. Classical Vs Deep Learning Models 1. If – Else Rules (Chatbot) 2. Audio Frequency Components Analysis (Speech Recognition) 3. Bag-of-Words Model (Classification) 4. CNN for Text Recognition (Classification) 5. Sequence to Sequence (Many Applications)

5. Bag of Words

6. Bag of Words Yes/No

7. Bag of Words

8. Bag of Words

9. Bag of Words

10. Bag of Words

11. Bag of Words

12. Bag of Words

13. Bag of Words

14. Natural Language Processing-steps 1. Importing the Libraries 2. Importing the Dataset 3. Cleaning the Texts 4. Creating the Bag-of-words Model 5. Splitting the Dataset into Training and Test Sets 6. Training the Naïve Bayes Model on the Training Set 7. Predicting the Test Set Result 8. Making the Confusion Matrix.

15. Restaurant_Reviews

16. NLP Python Code

17. NLP Python Code

18. NLP Python Code

19. NLP Python Code

20. NLP Python Code

21. Predicting if a single review is positive or negative Use the model to predict if the following review: "I love this restaurant so much“ is positive or negative. Positive Review Negative Review Use the model to predict if the following review: "I hate this restaurant so much“ is positive or negative.

22. Predicting if a single review is positive or negative

23. Predicting if a single review is positive or negative

Natural Language Processing Algorithm...

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