| 1. Ambiguities in NLP-en_US.srt | 5.6 KB | ||
| 1. Ambiguities in NLP.mp4 | 17.4 MB | ||
| 1. Case Study Sentiment Analysis & Word Cloud-en_US.srt | 11.1 KB | ||
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| 1. Introduction to Colab Google Cloud Development Environment-en_US.srt | 8.3 KB | ||
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| 1. Key concepts in NLP Sentence Segmentation-en_US.srt | 2.7 KB | ||
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| 1. NLTK and NLP in action-en_US.srt | 7.3 KB | ||
| 1. NLTK and NLP in action.mp4 | 31.8 MB | ||
| 10. Tuple-en_US.srt | 5.3 KB | ||
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| 13. Getting Started with NumPy-en_US.srt | 8.7 KB | ||
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| 14. NumPy Shape in Arrays-en_US.srt | 4 KB | ||
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| 18. NumPy Searching and Sorting Arrays-en_US.srt | 2.7 KB | ||
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| 19. Getting Started with Pandas-en_US.srt | 7.3 KB | ||
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| 22. Pandas Sorting, Slicing, Flipping, Grouping Data-en_US.srt | 7.2 KB | ||
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| 5. Named Entity Recognition (NER)-en_US.srt | 2.5 KB | ||
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| 6. Key concepts in NLP Dependency Parsing-en_US.srt | 1.3 KB | ||
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| 7. Key concepts in NLP Parts of Speech-en_US.srt | 3 KB | ||
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| 8. Arrays-en_US.srt | 4 KB | ||
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| 9. List-en_US.srt | 4.5 KB | ||
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| Bonus Resources.txt | 307.2 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 84 total files | |||
Master Natural Language Processing (NLP) with Python
MP4 | Video: h264, 1280x720 | Audio: AAC, 44100 Hz
Language: English | Size: 739 MB | Duration: 2h 43m
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.
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.
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