Natural Language Processing - Basic to Advance using Python
- 3.3
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Brief Introduction
Learn NLP Basic to Advance (using ML & DL) in Python. Become NLP professional by learning from NLP professionalDescription
As practitioner of NLP, I am trying to bring many relevant topics under one umbrella in following topics. The NLP has been most talked about for last few years and the knowledge has been spread across multiple places.
1. The content (80% hands on and 20% theory) will prepare you to work independently on NLP projects
2. Learn - Basic, Intermediate and Advance concepts
3. NLTK, regex, Stanford NLP, TextBlob, Cleaning
4. Entity resolution
5. Text to Features
6. Word embedding
7. Word2vec and GloVe
8. Word Sense Disambiguation
9. Speech Recognition
10. Similarity between two strings
11. Language Translation
12. Computational Linguistics
13. Classifications using Random Forest, Naive Bayes and XgBoost
14. Classifications using DL with Tensorflow (tf.keras)
15. Sentiment analysis
16. K-means clustering
17. Topic modeling
18. How to know models are good enough Bias vs Variance
Requirements
- Requirements
- Awareness of Machine Learning and Deep Learning concepts using Python
Knowledge
- 1. The content (80% hands on and 20% theory) will prepare you to work independently on NLP projects
- 2. Learn - Basic, Intermediate and Advance concepts
- 3. NLTK, regex, Stanford NLP, TextBlob, Cleaning
- 4. Entity resolution
- 5. Text to Features
- 6. Word embedding
- 7. Word2vec and GloVe
- 8. Word Sense Disambiguation
- 9. Speech Recognition
- 10. Similarity between two strings
- 11. Language Translation
- 12. Computational Linguistics
- 13. Classifications using Random Forest, Naive Bayes and XgBoost
- 14. Classifications using DL with Tensorflow (tf keras)
- 15. Sentiment analysis
- 16. K-means clustering
- 17. Topic modeling
- 18. How to know models are good enough Bias vs Variance
Shiv Onkar Deepak Kumar
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