Natural Language Processing Basic to Advance using Python

Natural Language Processing – Basic to Advance using Python, available at $39.99, has an average rating of 4.25, with 53 lectures, based on 18 reviews, and has 161 subscribers.

You will learn about 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 This course is ideal for individuals who are Anyone who want to Learn and Apply NLP using Python It is particularly useful for Anyone who want to Learn and Apply NLP using Python.

Enroll now: Natural Language Processing – Basic to Advance using Python

Summary

Title: Natural Language Processing – Basic to Advance using Python

Price: $39.99

Average Rating: 4.25

Number of Lectures: 53

Number of Published Lectures: 53

Number of Curriculum Items: 53

Number of Published Curriculum Objects: 53

Original Price: $22.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

Anyone who want to Learn and Apply NLP using Python

Target Audiences

Anyone who want to Learn and Apply NLP using Python

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

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction and Walk through of contents

Lecture 2: Presentation ppt and Python code

Lecture 3: Installations and Technology

Lecture 4: Various Libraries

Lecture 5: What Is Natural Language Processing

Lecture 6: Applications of NLP

Chapter 2: Basic

Lecture 1: Basic string operations

Lecture 2: Basic regex

Lecture 3: NLTK Install and Testing

Lecture 4: NLTK Tokenizers

Lecture 5: NLTK Part-of-speech tagging

Lecture 6: NLTK Stemming and Lemmatization

Lecture 7: NLTK Word-sense disambiguation

Lecture 8: NLTK BLEU Scores

Lecture 9: Stanford NLP

Lecture 10: TextBlob

Lecture 11: Miscellaneous

Lecture 12: String Cleaning part1

Lecture 13: String Cleaning part2

Lecture 14: String Cleaning part3

Lecture 15: String Cleaning part4

Lecture 16: WordCloud

Chapter 3: Intermediate

Lecture 1: Overall approach for NLP solutions

Lecture 2: Entity resolution or Deduplication

Lecture 3: Entity resolution or Deduplication – data prep

Lecture 4: Entity resolution or Deduplication – single table

Lecture 5: Entity resolution or Deduplication – two tables

Lecture 6: Text to Features – One hot encoding

Lecture 7: Count vectorizer

Lecture 8: TF-IDF (Term Frequency, Inverse Document Frequency)

Lecture 9: Word embedding

Lecture 10: Word2vec and GloVe

Lecture 11: Word embedding of custom review data

Lecture 12: Word Sense Disambiguation

Lecture 13: Speech Recognition using Microphone

Lecture 14: Speech Recognition using Audio Files

Lecture 15: Similarity between two strings

Lecture 16: Language Translation

Lecture 17: Computational Linguistics

Lecture 18: Computational Linguistics – Dependency Extraction

Chapter 4: Advance

Lecture 1: Advance – Introductions

Lecture 2: Classifications using Random Forest

Lecture 3: Classifications using Naive Bayes and XgBoost

Lecture 4: Classifications using DL with tfkeras MLP

Lecture 5: Classifications using DL with tfkeras inbuilt embedded layer

Lecture 6: Classifications using DL with tfkeras WordVector transformed to average

Lecture 7: Classifications using DL with tfkeras custom WordVector

Lecture 8: How to know models are good enough Bias vs Variance

Lecture 9: Sentiment analysis

Lecture 10: K-means clustering

Lecture 11: Topic modeling

Lecture 12: Search engine

Lecture 13: Miscellaneous

Instructors

Shiv Onkar Deepak Kumar
AI Researcher and Consultant, Chief Data Scientist

Rating Distribution

1 stars: 3 votes

2 stars: 1 votes

3 stars: 3 votes

4 stars: 2 votes

5 stars: 9 votes

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