English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 1h 47m | 321 MB
Learn and master the NLTK library in Python to create your own NLP apps
NLP, or Natural Language Processing, is a computational approach to communication. This course will get you up-and-running with the popular NLP platform called Natural Language Toolkit (NLTK) in no time. You will start off by preparing text for Natural Language Processing by cleaning and simplifying it. Then you will implement more complex algorithms to break this text down and uncover contextual relationships that reveal the meaning and content of the text.
You will learn how to tokenize various parts of sentences, and how to analyze them. You will learn about semantic as well as the syntactic analysis of text. During this course, you will learn how to solve various ambiguities in processing human language. You will also gain experience with NLP using Python and will be introduced to a variety of useful tools in NLTK. Plus, you will have an opportunity to build your first NLP application!
By the end of this course, you will have the skills and tools to begin solving problems in the growing field of Latent Semantic Analysis
This video course covers various topics in Natural Language Processing, ranging from an introduction to the relevant Python libraries to applying specific linguistics concepts while exploring text datasets. It is evenly-paced for simplicity and does not require prior knowledge of NLP theory.
What You Will Learn
- Installing and setting up NLTK, and how to implement simple NLP tasks
- The foundational concepts of part-of-speech tagging
- Stemming, lemmatization, and named-entity recognition (NER)
- Discover how to create frequency distributions on your text with NLTK
- Analyze text and classify it into different categories
- Use functions to implement concordance, similarity, dispersion plotting, and counting in NLTK to easily mine information from large heaps of textual data
- Build your own movie review sentiment application in Python
- Learn how to classify user reviews as positive or negative with sentiment analysis
- See how your application, based on bag-of-words, can retrieve meaningful information
- Apply Latent Semantic Analysis to extract the meaning of the text in response to user queries
- Use Long Shot Term Memory to analyze sequential data in your NLP applications
Table of Contents
01 The Course Overview
02 Installing and Setting Up NLTK
03 Implementing Simple NLP Tasks and Exploring NLTK Libraries
04 Part-Of-Speech Tagging
05 Stemming and Lemmatization
06 Named Entity Recognition
07 Frequency Distribution with NLTK
08 Frequency Distribution on Your Text with NLTK
09 Concordance Function in NLTK
10 Similar Function in NLTK
11 Dispersion Plot Function in NLTK
12 Count Function in NLTK
13 Introduction to Recurrent Neural Network and Long Short Term Memory
14 Programming Your Own Sentiment Classifier Using NLTK
15 Perform Sentiment Classification on a Movie Rating Dataset
16 Starting with Latent Semantic Analysis
17 Programming Example of Principal Component Analysis
18 Programming Example of Singular Value Decomposition
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