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Natural Language Processing - Basic to Advance using Python
Rating: 3.7 out of 5(20 ratings)
181 students

Natural Language Processing - Basic to Advance using Python

Learn NLP Basic to Advance (using ML & DL) in Python. Become NLP professional by learning from NLP professional
Created byShiv Onkar Deepak Kumar
Last updated 2/2022
English

What you'll learn

  • 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

Course content

4 sections • 53 lectures • 7h 10m total length

  • Introduction and Walk through of contents3:34
  • Presentation ppt and Python code0:47
  • Installations and Technology4:58
  • Various Libraries2:34
  • What Is Natural Language Processing5:44
  • Applications of NLP3:31

Requirements

  • Awareness of Machine Learning and Deep Learning concepts using Python

Description

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

Who this course is for:

  • Anyone who want to Learn and Apply NLP using Python

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