| 1 Unicode Python 2.ipynb | 5.6 KB | ||
| 1. Course Summary.mp4 | 12.3 MB | ||
| 1. Course Summary.srt | 4 KB | ||
| 1. Introduction.mp4 | 29.5 MB | ||
| 1. Introduction.srt | 5.3 KB | ||
| 2 Unicode Python 3.ipynb | 1.7 KB | ||
| 2. Classification with TextBlob.mp4 | 141.8 MB | ||
| 2. Classification with TextBlob.srt | 16 KB | ||
| 2. Course Material & Source Code.html | 0 B | ||
| 2. Learn About LDA Gensim.mp4 | 95.6 MB | ||
| 2. Learn About LDA Gensim.srt | 11.6 KB | ||
| 2. Learn About Word Vectors.mp4 | 21.1 MB | ||
| 2. Learn About Word Vectors.srt | 5.7 KB | ||
| 2. Learn About pyspotlight.mp4 | 32.2 MB | ||
| 2. Learn About pyspotlight.srt | 4.8 KB | ||
| 2. Learn How to Work with Unicode.mp4 | 51.2 MB | ||
| 2. Learn How to Work with Unicode.srt | 7.8 KB | ||
| 2. Learn and Understand Sentence Head.mp4 | 21.1 MB | ||
| 2. Learn and Understand Sentence Head.srt | 3.7 KB | ||
| 2. Machine Learning - Sentiment In VADER.mp4 | 65.4 MB | ||
| 2. Machine Learning - Sentiment In VADER.srt | 8.4 KB | ||
| 2. Text To Symbols - Splitting Sentences.mp4 | 40.2 MB | ||
| 2. Text To Symbols - Splitting Sentences.srt | 5.3 KB | ||
| 3. Classification with scikit-learn.mp4 | 94.7 MB | ||
| 3. Classification with scikit-learn.srt | 11.2 KB | ||
| 3. Learn About FRED.mp4 | 34.5 MB | ||
| 3. Learn About FRED.srt | 5.1 KB | ||
| 3. Learn About Google Word Vectors.mp4 | 50.8 MB | ||
| 3. Learn About Google Word Vectors.srt | 6.2 KB | ||
| 3. Learn About LDA pyLDAvis.mp4 | 47.9 MB | ||
| 3. Learn About LDA pyLDAvis.srt | 6 KB | ||
| 3. Learn and Understand Named Entities.mp4 | 29.7 MB | ||
| 3. Learn and Understand Named Entities.srt | 5.2 KB | ||
| 3. Text To Symbols - Filtering Stop Words.mp4 | 25.1 MB | ||
| 3. Text To Symbols - Filtering Stop Words.srt | 3.3 KB | ||
| 4. Subsymbolic - Learn Word Vectors.mp4 | 106.5 MB | ||
| 4. Subsymbolic - Learn Word Vectors.srt | 13.3 KB | ||
| Bonus Resources.txt | 307.2 B | ||
| Dependency Parsing.ipynb | 5.6 KB | ||
| Entity Recognition.ipynb | 4.5 KB | ||
| FRED.ipynb | 2.8 KB | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| Head of a Sentence.ipynb | 2.9 KB | ||
| LDA_with_gensim.ipynb | 21.7 KB | ||
| No Work Files.txt | 0 B | ||
| Sentiment_VADER.ipynb | 4.2 KB | ||
| Text_Classification_With_TextBlob.ipynb | 14.5 KB | ||
| Text_Classification_with_scikit-learn.ipynb | 88.6 KB | ||
| Train Word Vectors2.ipynb | 23.5 KB | ||
| Use Google Word Vectors.ipynb | 29.4 KB | ||
| our_textblob_classifiers.py | 20.1 KB | ||
| pyLDAvis.ipynb | 155.4 KB | ||
| pyspotlight.ipynb | 3.1 KB | ||
| sentences NLTK spaCy.ipynb | 3.4 KB | ||
| stop words in NLTK.ipynb | 6.3 KB | ||
| string processing.ipynb | 7.1 KB | ||
| tf-idf Gensim.ipynb | 11.2 KB | ||
| tokens NLTK spaCy.ipynb | 7.9 KB | ||
| ▲ 75 total files | |||
AI with Python - Natural Language Processing (NLP) (Updated)
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 26 lectures (1h 52m) | Size: 1.17 GB
Maximize your NLP capabilities while creating amazing NLP projects in Python
What you'll learn:
Core Concepts
Convert Text to Symbols
Developing a Text Classifier
Vector Representation
Learn basic string processing in python
Learn how to tokenize text so it can be processed as symbols
Identify the grammatical parts of a sentence
Understand the capabilities and limitations of NLP
Requirements
Basic Python programming knowledge is helpful but not required
Description
Python is the most widely used language for natural language processing (NLP) thanks to its extensive tools and libraries for analyzing text and extracting computer-usable data. Natural Language Processing is one of the fields of computational linguistics and artificial intelligence that is concerned with human-computer interaction. It provides a seamless interaction between computers and human beings and gives computers the ability to understand human speech with the help of machine learning.
This course will take you through a range of techniques for text processing, from basics such as parsing the parts of speech to complex topics such as topic modeling, text classification, and visualization. The course starts with an introduction to NLP. You’ll study different approaches to NLP tasks, and perform exercises in Python to understand the process of preparing datasets for NLP models. Next, you’ll use advanced NLP algorithms and visualization techniques to collect datasets from open websites, and to summarize and generate random text from a document. In the final chapters, you’ll use NLP to create a chatbot that detects positive or negative sentiment in text documents such as movie reviews.
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