| 1. Ambiguities in NLP.mp4 | 17.4 MB | ||
| 1. Ambiguities in NLP.srt | 5.9 KB | ||
| 1. Case Study Sentiment Analysis & Word Cloud.mp4 | 54.1 MB | ||
| 1. Case Study Sentiment Analysis & Word Cloud.srt | 11.6 KB | ||
| 1. Infrastructure for Streamlit.mp4 | 9.1 MB | ||
| 1. Infrastructure for Streamlit.srt | 1.6 KB | ||
| 1. Introduction to Colab Google Cloud Development Environment.mp4 | 25.9 MB | ||
| 1. Introduction to Colab Google Cloud Development Environment.srt | 8.7 KB | ||
| 1. Introduction.mp4 | 11.3 MB | ||
| 1. Introduction.srt | 1.5 KB | ||
| 1. Key concepts in NLP Sentence Segmentation.mp4 | 13.2 MB | ||
| 1. Key concepts in NLP Sentence Segmentation.srt | 2.8 KB | ||
| 1. Machine Learning Concepts.mp4 | 149 MB | ||
| 1. Machine Learning Concepts.srt | 36 KB | ||
| 1. NLTK and NLP in action.mp4 | 31.8 MB | ||
| 1. NLTK and NLP in action.srt | 7.6 KB | ||
| 10. Tuple.mp4 | 19.8 MB | ||
| 10. Tuple.srt | 5.6 KB | ||
| 11. Set.mp4 | 19.6 MB | ||
| 11. Set.srt | 5 KB | ||
| 12. Dictionary.mp4 | 13 MB | ||
| 12. Dictionary.srt | 2.9 KB | ||
| 13. Getting Started with NumPy.mp4 | 28.5 MB | ||
| 13. Getting Started with NumPy.srt | 9.1 KB | ||
| 14. NumPy Shape in Arrays.mp4 | 12.7 MB | ||
| 14. NumPy Shape in Arrays.srt | 4.2 KB | ||
| 15. NumPy Iterating Arrays.mp4 | 5.7 MB | ||
| 15. NumPy Iterating Arrays.srt | 1.6 KB | ||
| 16. NumPy Joining Arrays.mp4 | 13.4 MB | ||
| 16. NumPy Joining Arrays.srt | 2.9 KB | ||
| 17. NumPy Splitting Arrays.mp4 | 6.1 MB | ||
| 17. NumPy Splitting Arrays.srt | 1.8 KB | ||
| 18. NumPy Searching and Sorting Arrays.mp4 | 10.3 MB | ||
| 18. NumPy Searching and Sorting Arrays.srt | 2.8 KB | ||
| 19. Getting Started with Pandas.mp4 | 34.3 MB | ||
| 19. Getting Started with Pandas.srt | 7.7 KB | ||
| 2. Case Study Speech to Text deployment in a call center.mp4 | 37.3 MB | ||
| 2. Case Study Speech to Text deployment in a call center.srt | 9.4 KB | ||
| 2. Creating a very simple web app and Getting started with streamlit.mp4 | 24.6 MB | ||
| 2. Creating a very simple web app and Getting started with streamlit.srt | 6.4 KB | ||
| 2. Getting Started with Python.mp4 | 13.7 MB | ||
| 2. Getting Started with Python.srt | 4.8 KB | ||
| 2. Key concepts in NLP Word Tokenization.mp4 | 7.4 MB | ||
| 2. Key concepts in NLP Word Tokenization.srt | 1.9 KB | ||
| 2. Logistic Regression and Introduction to Deep Learning.mp4 | 219.1 MB | ||
| 2. Logistic Regression and Introduction to Deep Learning.srt | 47.4 KB | ||
| 2. Noise removal.mp4 | 9.8 MB | ||
| 2. Noise removal.srt | 1.8 KB | ||
| 20. Pandas Dataframe.mp4 | 28.1 MB | ||
| 20. Pandas Dataframe.srt | 6.7 KB | ||
| 21. Pandas Descriptive Statistics.mp4 | 10.8 MB | ||
| 21. Pandas Descriptive Statistics.srt | 3.7 KB | ||
| 22. Pandas Sorting, Slicing, Flipping, Grouping Data.mp4 | 22.7 MB | ||
| 22. Pandas Sorting, Slicing, Flipping, Grouping Data.srt | 7.5 KB | ||
| 23. Data Visualization using Matplotlib.mp4 | 33 MB | ||
| 23. Data Visualization using Matplotlib.srt | 8.8 KB | ||
| 3. Header and Sub Header.mp4 | 13.5 MB | ||
| 3. Header and Sub Header.srt | 3.7 KB | ||
| 3. Key concepts in NLP Stemming.mp4 | 9 MB | ||
| 3. Key concepts in NLP Stemming.srt | 1.9 KB | ||
| 3. Spacy.mp4 | 11.2 MB | ||
| 3. Spacy.srt | 2.6 KB | ||
| 3. Text Summarization.mp4 | 24.4 MB | ||
| 3. Text Summarization.srt | 4.1 KB | ||
| 3. Variables.mp4 | 24.6 MB | ||
| 3. Variables.srt | 7.7 KB | ||
| 4. Case Study 4 Spam Classification Using Machine Learning.mp4 | 29.8 MB | ||
| 4. Case Study 4 Spam Classification Using Machine Learning.srt | 6.8 KB | ||
| 4. Flash Text.mp4 | 10.1 MB | ||
| 4. Flash Text.srt | 1.5 KB | ||
| 4. Key concepts in NLP Lemmatization.mp4 | 6.5 MB | ||
| 4. Key concepts in NLP Lemmatization.srt | 1.7 KB | ||
| 4. Operators.mp4 | 18.8 MB | ||
| 4. Operators.srt | 5.6 KB | ||
| 4. Reading and displaying contents of a file.mp4 | 11.8 MB | ||
| 4. Reading and displaying contents of a file.srt | 3.3 KB | ||
| 5. Conditions.mp4 | 18.3 MB | ||
| 5. Conditions.srt | 6.7 KB | ||
| 5. Key concepts in NLP Stop Words.mp4 | 6.7 MB | ||
| 5. Key concepts in NLP Stop Words.srt | 2.1 KB | ||
| 5. Named Entity Recognition (NER).mp4 | 13.3 MB | ||
| 5. Named Entity Recognition (NER).srt | 2.6 KB | ||
| 5. Uploading a file.mp4 | 9.8 MB | ||
| 5. Uploading a file.srt | 2.9 KB | ||
| 6. Key concepts in NLP Dependency Parsing.mp4 | 4.6 MB | ||
| 6. Key concepts in NLP Dependency Parsing.srt | 1.4 KB | ||
| 6. Loops.mp4 | 19.9 MB | ||
| 6. Loops.srt | 7.6 KB | ||
| 6. NLP Wordcloud App.mp4 | 38.1 MB | ||
| 6. NLP Wordcloud App.srt | 7.7 KB | ||
| 7. Deploying the app in Heroku.mp4 | 52.7 MB | ||
| 7. Deploying the app in Heroku.srt | 10.7 KB | ||
| 7. Functions.mp4 | 15.3 MB | ||
| 7. Functions.srt | 5.5 KB | ||
| 7. Key concepts in NLP Parts of Speech.mp4 | 11.2 MB | ||
| 7. Key concepts in NLP Parts of Speech.srt | 3.1 KB | ||
| 8. Arrays.mp4 | 20.1 MB | ||
| 8. Arrays.srt | 4.1 KB | ||
| 8. Deploying the app in streamlit.mp4 | 18.7 MB | ||
| 8. Deploying the app in streamlit.srt | 4.9 KB | ||
| 9. List.mp4 | 15.4 MB | ||
| 9. List.srt | 4.7 KB | ||
| Bonus Resources.txt | 307.2 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 104 total files | |||
Master Natural Language Processing & Build NLP Web App
Last Update: 7/2021
Duration: 4h 32m | Video: .MP4, 1280x720 30 fps | Audio: AAC, 44.1 kHz, 2ch | Size: 1.25 GB
Genre: eLearning | Language: English
Create Word Cloud App Using Streamlit | Sentiment Analysis | Speech to text | Spam Detection | Code Walkthrough
What you'll learn:
You will gain insights on what Natural Language Processing(NLP) is, its Applications & Challenges
You will learn Sentence Segmentation, Word Tokenization, Stemming, Lemmatization, Parsing, POS & Ambiguities in NLP
You will learn to execute using Machine Learning, NLTK & Spacey
You will learn to work with Text Files with Python
You will utilize Regular Expressions for pattern searching in text
You will use Part of Speech Tagging to automatically process raw text files
You will visualize POS and NER with Spacy
You will understand Vocabulary Matching with Spacy
You will use NLTK for Sentiment Analysis
Requirements:
None. (Python is covered extensively in the course)
Description:
Natural Language Processing (NLP) is a very interesting field associated with AI and is at the forefront of many useful applications like a chatbot. Knowledge of NLP is considered a necessity for those pursuing a career in AI. This course covers both the theory as well as the applications of NLP. Case studies are explained along with a walkthrough of the codes for a better understanding of the subject.
A detailed explanation of how to build a web app for NLP using Streamlit is also explained.
NLP is a subfield of computer science and artificial intelligence concerned with interactions between computers and human (natural) languages. It is used to apply machine learning algorithms to text and speech.
For example, we can use NLP to create systems like speech recognition, document summarization, machine translation, spam detection, named entity recognition, question answering, autocomplete, predictive typing and so on.
Nowadays, most of us have smartphones that have speech recognition. These smartphones use NLP to understand what is said. Also, many people use laptops whose operating system has built-in speech recognition.
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[ FreeCourseWeb ] Udemy - Master Natural Language Processing (NLP) with Python Posted by
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