基于Python和自然语言处理的自然语言处理 Natural Language Processing (NLP) with Python and NLTK

实用方法:收集和预处理文本数据，数据可视化，模型构建和自然语言处理应用

你会学到什么
使用Python和NLTK的自然语言处理的概念和实际应用。

要求
Python，Numpy，Pandas，Matplotlib和线性代数

MP4 |视频:h264，1280×720 |音频:AAC，44.1 KHz
语言：英语+中英文字幕（云桥CG资源站 机译）|大小:2.65 GB |时长:7h 14m

描述
自然语言处理是机器学习领域的一个热门话题。


本课程侧重于实践方法，有许多例子和开发功能应用。本课程开始向您解释如何获得编码的基本工具，并回顾主要的机器学习概念和算法。之后，本课程将向您全面介绍自然语言处理中的主要工具，如:文本数据组装、文本数据预处理、文本数据可视化、模型构建以及最终开发自然语言处理应用程序。

关于自然语言处理的热门话题是

-正则表达式-废弃网络

-用于提取文本内容的文本摘要库

-句子拆分和标记化

-词干和引理化

-停止和罕见的单词删除

-词性标注

-组块

-纳克

-单词包:TfidfVectorizer

-频率表

-共生矩阵

-字云库

-文本相似性

-文本聚类

-潜在语义分析

-主题建模

-文本分类

-情感分析

– Word2Vec库

-推荐系统:协同过滤

-垃圾邮件检测程序

-推特上的社交媒体挖掘

还有更多！…

在本课程中，您会发现对该理论的简要回顾以及图形解释，对于编码，它使用Python语言和NLTK库。

最后，本课程为您的实践和学习提供了许多数据集和其他资源。

学生有机会通过问答论坛、电子邮件:machine.learning.eirl@gmail.com或推特:@ AILearningCQ获得讲师的反馈

这门课是给谁的
对自然语言处理及其应用感兴趣的专业人士

MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.50 GB | Duration: 7h 14m

Practical Approach : Collecting and Preprocessing text data, Data Visualization, Model Building and NLP Apps

What you’ll learn
Concepts and practical applications of Natural Language Processing using Python and NLTK.

Requirements
Python, Numpy, Pandas, Matplotlib and Linear algebra
Description
Natural Language Processing (NLP) is a hot topic into the Machine Learning field.

This course is focused in practical approach with many examples and developing functional applications. This course starts explaining you, how to get the basic tools for coding and also making a review of the main machine learning concepts and algorithms. After that this course offers you a complete explanation of the main tools in NLP such as: Text Data Assemble, Text Data Preprocessing, Text Data Visualization, Model Building and finally developing NLP applications.

Hot topics on NLP that I will cover with practical applications on this course are

– Regular expressions – Scrapping the web

– Textract library for extracting text content

– Sentence splitter and tokenization

– Stemming and Lemmatization

– Stop and rare word removal

– Part of Speech (POS) tagging

– Chunking

– N-grams

– Bag of Words: TfidfVectorizer

– Frequency Chart

– Co-occurence matrix

– Word cloud library

– Text similarity

– Text clustering

– Latent Semantic Analysis

– Topic Modeling

– Text Classification

– Sentiment Analysis

– Word2Vec library

– Recommender Systems: Collaborative Filtering

– Spam detector app

– Social Media Mining on Twitter

and much more!…

In this course you will find a concise review of the theory with graphical explanations and for coding it uses Python language and NLTK library.

Finally this course offers you many datasets and other resources for your practice and study.

The student has the opportunity to get a feedback from the instructor through Q&A forums, by email: machine.learning.eirl@gmail.com or by Twitter: @AILearningCQ

Who this course is for
Professionals with interest in Natural Language Processing topic and it’s applications
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