Data Science: Natural Language Processing (NLP) in Python 2021

在本课程中，您将使用自然语言处理或 NLP（处理文本和语音的机器学习和数据科学的分支）构建多个实用系统。这门课程不是我的深度学习系列的一部分，所以它不包含任何难的数学——只是直接用 Python 编码。本课程的所有材料都是免费的。

在简要讨论 NLP 是什么以及它可以做什么之后，我们将开始构建非常有用的东西。我们将构建的第一件事是密码解密算法。这些在战争和间谍活动中都有应用。我们将在本节中学习如何构建和应用几种有用的 NLP 工具，即字符级语言模型（使用马尔可夫原理）和遗传算法。

我们开始使用更传统的“机器学习”的第二个项目是构建垃圾邮件检测器。与 2000 年代初期相比，由于此类系统，如今您收到的垃圾邮件可能很少。

接下来，我们将在 Python 中构建一个用于情感分析的模型。这允许我们为一段文本分配一个分数，告诉我们它的积极或消极程度。人们在 Twitter 上使用情绪分析来预测股市。

我们将介绍一些实用的工具和技术，如 NLTK（自然语言工具包）库和潜在语义分析或 LSA。

最后，我们通过构建文章微调器来结束课程。这是一个非常棘手的问题，即使是当今最流行的产品也无法解决问题。这些讲座旨在让您入门，并为您提供有关如何自己改进的想法。掌握后，您可以将其用作 SEO 或搜索引擎优化工具。如果你能为他们做这件事，各地的网络营销人员都会爱你！

本课程侧重于“如何构建和理解”，而不仅仅是“如何使用”。阅读一些文档后，任​​何人都可以在 15 分钟内学会使用 API。这不是关于“记住事实”，而是关于通过实验“亲眼看到”。它将教您如何可视化模型内部发生的事情。如果您想要的不仅仅是对机器学习模型的肤浅了解，那么本课程适合您。

MP4 | Video: h264, 1280×720 | Audio: AAC, 44100 Hz
Language: English | Size: 2.56 GB | Duration: 9h 55m

What you’ll learn
Write your own cipher decryption algorithm using genetic algorithms and language modeling with Markov models
Write your own spam detection code in Python
Write your own sentiment analysis code in Python
Perform latent semantic analysis or latent semantic indexing in Python
Have an idea of how to write your own article spinner in Python

Requirements
Install Python, it’s free!
You should be at least somewhat comfortable writing Python code
Know how to install numerical libraries for Python such as Numpy, Scipy, Scikit-learn, Matplotlib, and BeautifulSoup
Take my free Numpy prerequisites course (it’s FREE, no excuses!) to learn about Numpy, Matplotlib, Pandas, and Scikit-Learn, as well as Machine Learning basics
Optional: If you want to understand the math parts, linear algebra and probability are helpful
Description
In this course you will build MULTIPLE practical systems using natural language processing, or NLP – the branch of machine learning and data science that deals with text and speech. This course is not part of my deep learning series, so it doesn’t contain any hard math – just straight up coding in Python. All the materials for this course are FREE.

After a brief discussion about what NLP is and what it can do, we will begin building very useful stuff. The first thing we’ll build is a cipher decryption algorithm. These have applications in warfare and espionage. We will learn how to build and apply several useful NLP tools in this section, namely, character-level language models (using the Markov principle), and genetic algorithms.

The second project, where we begin to use more traditional “machine learning”, is to build a spam detector. You likely get very little spam these days, compared to say, the early 2000s, because of systems like these.

Next we’ll build a model for sentiment analysis in Python. This is something that allows us to assign a score to a block of text that tells us how positive or negative it is. People have used sentiment analysis on Twitter to predict the stock market.

We’ll go over some practical tools and techniques like the NLTK (natural language toolkit) library and latent semantic analysis or LSA.

Finally, we end the course by building an article spinner. This is a very hard problem and even the most popular products out there these days don’t get it right. These lectures are designed to just get you started and to give you ideas for how you might improve on them yourself. Once mastered, you can use it as an SEO, or search engine optimization tool. Internet marketers everywhere will love you if you can do this for them!

This course focuses on “how to build and understand”, not just “how to use”. Anyone can learn to use an API in 15 minutes after reading some documentation. It’s not about “remembering facts”, it’s about “seeing for yourself” via experimentation. It will teach you how to visualize what’s happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.

“If you can’t implement it, you don’t understand it”

Or as the great physicist Richard Feynman said: “What I cannot create, I do not understand”.

My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch

Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?

After doing the same thing with 10 datasets, you realize you didn’t learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times…

Suggested Prerequisites:

Python coding: if/else, loops, lists, dicts, sets

Take my free Numpy prerequisites course (it’s FREE, no excuses!) to learn about Numpy, Matplotlib, Pandas, and Scikit-Learn, as well as Machine Learning basics

Optional: If you want to understand the math parts, linear algebra and probability are helpful

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture “Machine Learning and AI Prerequisite Roadmap” (available in the FAQ of any of my courses, including the free Numpy course)

Who this course is for:
Students who are comfortable writing Python code, using loops, lists, dictionaries, etc.
Students who want to learn more about machine learning but don’t want to do a lot of math
Professionals who are interested in applying machine learning and NLP to practical problems like spam detection, Internet marketing, and sentiment analysis
This course is NOT for those who find the tasks and methods listed in the curriculum too basic.
This course is NOT for those who don’t already have a basic understanding of machine learning and Python coding (but you can learn these from my FREE Numpy course).
This course is NOT for those who don’t know (given the section titles) what the purpose of each task is. E.g. if you don’t know what “spam detection” might be useful for, you are too far behind to take this course.