Master Natural Language Processing & Build NLP Web App

自然语言处理 (NLP) 是一个与 AI 相关的非常有趣的领域，并且处于聊天机器人等许多有用应用程序的前沿。NLP 的知识被认为是从事 AI 职业的人的必要条件。本课程涵盖了 NLP 的理论和应用。案例研究与代码演练一起解释，以便更好地理解该主题。
还解释了如何使用 Streamlit 为 NLP 构建 Web 应用程序的详细说明。
NLP 是计算机科学和人工智能的一个子领域，涉及计算机与人类（自然）语言之间的交互。它用于将机器学习算法应用于文本和语音。
例如，我们可以使用 NLP 创建语音识别、文档摘要、机器翻译、垃圾邮件检测、命名实体识别、问答、自动完成、预测输入等系统。
如今，我们大多数人都拥有具有语音识别功能的智能手机。这些智能手机使用 NLP 来理解所说的内容。此外，许多人使用操作系统具有内置语音识别功能的笔记本电脑。

一些示例：
1.Cortana
Microsoft 操作系统有一个名为 Cortana 的虚拟助手，可以识别自然语音。您可以使用它来设置提醒、打开应用程序、发送电子邮件、玩游戏、跟踪航班和包裹、查看天气等。
2.Siri
Siri 是苹果公司的 iOS、watchOS、macOS、HomePod 和 tvOS 操作系统的虚拟助手。同样，您可以使用语音命令做很多事情：拨打电话、给某人发短信、发送电子邮件、设置计时器、拍照、打开应用程序、设置闹钟、使用导航等等。
3.Gmail
Google 开发的著名电子邮件服务 Gmail 就是使用垃圾邮件检测来过滤掉一些垃圾邮件。

在本课程中，我们将处理：
a) NLP 介绍：
什么是 NLP
应用 NLP
挑战 NLP

b) NLP 中的关键概念：
句子分割
Word Tokenization
Stemming
Lemmatization
Parsing
POS
Ambiguities in NLP

C）NLP在行动
NLTK
句子符号化
字符号化
词干
词形还原
去噪
Spacy
词性标注的零件
依存分析
拼写校正
视角下的
正则表达式
Flash文本
命名实体识别- NER

Last Update: 7/2021
Duration: 4h 32m | Video: .MP4, 1280×720 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.

Some Examples:
1.Cortana
The Microsoft OS has a virtual assistant called Cortana that can recognize a natural voice. You can use it to set up reminders, open apps, send emails, play games, track flights and packages, check the weather and so on.
2.Siri
Siri is a virtual assistant of the Apple Inc.’s iOS, watchOS, macOS, HomePod, and tvOS operating systems. Again, you can do a lot of things with voice commands: start a call, text someone, send an email, set a timer, take a picture, open an app, set an alarm, use navigation and so on.
3.Gmail
The famous email service Gmail developed by Google is using spam detection to filter out some spam emails.

In this course we will deal with:
a)NLP Introduction:
What is NLP
Applications of NLP
Challenges in NLP

b)Key concepts in NLP:
Sentence Segmentation
Word Tokenization
Stemming
Lemmatization
Parsing
POS
Ambiguities in NLP

c)NLP in Action
NLTK
Sentence Tokenization
Word Tokenization
Stemming
Lemmatization
Noise Removal
Spacy
Parts of Speech Tagging
Dependency Parsing
Spell Correction
Point of View
Regular Expressions
Flash Text
Named Entity Recognition – NER

d)Case studies:
Speech recognition
Sentiment analysis
Word Cloud
Spam detection

You will not only get fantastic technical content with this course, but you will also get access to both our course-related Question and Answer forums, as well as our live student chat channel, so you can team up with other students for projects, or get help on the course content from myself and the course teaching assistants.
All of this comes with a 30-day money back guarantee, so you can try the course risk-free.
What are you waiting for? Become an expert in natural language processing today!
I will see you inside the course,

Who this course is for:
Data Scientists, Python Programmers, ML Practitioners, IT Managers managing data science projects
Python developers interested in learning how to use Natural Language Processing