4.67 out of 5
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9711 reviews on Udemy

Data Science: Natural Language Processing (NLP) in Python

Applications: decrypting ciphers, spam detection, sentiment analysis, article spinners, and latent semantic analysis.
Instructor:
Lazy Programmer Inc.
38,105 students enrolled
English [Auto] More
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

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)

Natural Language Processing - What is it used for?

1
Introduction and Outline
2
Why Learn NLP?
3
The Central Message of this Course (Big Picture Perspective)

Course Preparation

1
How to Succeed in this Course
2
Where to get the code and data
3
How to Open Files for Windows Users

Machine Learning Basics Review

1
Machine Learning: Section Introduction
2
What is Classification?
3
Classification in Code
4
What is Regression?
5
Regression in Code
6
What is a Feature Vector?
7
Machine Learning is Nothing but Geometry
8
All Data is the Same
9
Comparing Different Machine Learning Models
10
Machine Learning and Deep Learning: Future Topics
11
Section Summary

Decrypting Ciphers

1
Section Introduction
2
Ciphers
3
Language Models
4
Genetic Algorithms
5
Code Preparation
6
Link to Cipher Notebook
7
Code pt 1
8
Code pt 2
9
Code pt 3
10
Code pt 4
11
Code pt 5
12
Code pt 6
13
Section Conclusion

Build your own spam detector

1
Build your own spam detector - description of data
2
Build your own spam detector using Naive Bayes and AdaBoost - the code
3
Key Takeaway from Spam Detection Exercise
4
Naive Bayes Concepts
5
AdaBoost Concepts
6
Other types of features

In addition to the word frequencies we looked at previously, this lecture looks at bag-of-words in general and different ways to implement that, including raw counts and binary indicator variables. We also briefly mention TF-IDF.

7
Spam Detection FAQ (Remedial #1)
8
What is a Vector? (Remedial #2)
9
SMS Spam Example
10
SMS Spam in Code
11
Suggestion Box

Build your own sentiment analyzer

1
Description of Sentiment Analyzer

What is sentiment analysis? In this lecture we'll look at the data we'll be using to build our sentiment analysis tool, and talk about how we can manually pre-process the data so that we can plug it into a machine learning classifier.

2
Logistic Regression Review
3
Preprocessing: Tokenization
4
Preprocessing: Tokens to Vectors
5
Sentiment Analysis in Python using Logistic Regression

In this lecture we'll write our sentiment analyzer in Python to predict sentiment on Amazon reviews.

6
Sentiment Analysis Extension
7
How to Improve Sentiment Analysis & FAQ

NLTK Exploration

1
NLTK Exploration: POS Tagging

How do we tag the tokens of a sentence by their parts-of-speech? i.e. Is this token a noun, verb, adjective, adverb, or something else?

2
NLTK Exploration: Stemming and Lemmatization

How do we turn words into their "base form"? i.e. plural to singular

3
NLTK Exploration: Named Entity Recognition

Can we also tag parts of a sentence as a "person", "organization", or "location"?

4
Want more NLTK?

Latent Semantic Analysis

1
Latent Semantic Analysis - What does it do?

What is synonymy and polysemy and how can LSA / LSI help?

2
SVD - The underlying math behind LSA
3
Latent Semantic Analysis in Python
4
What is Latent Semantic Analysis Used For?
5
Extending LSA

Write your own article spinner

1
Article Spinning Introduction and Markov Models

What is article spinning and how is it related to search engines, SEO (search engine optimization), and Internet marketing?

2
Trigram Model

What will be our strategy for creating an article spinner? We'll create a trigram model.

3
More about Language Models
4
Precode Exercises
5
Writing an article spinner in Python
6
Article Spinner Extension Exercises

How to learn more about NLP

1
What we didn't talk about

Important NLP topics you should "know of" but that we didn't cover, and where you can learn more.

Setting Up Your Environment (FAQ by Student Request)

1
Windows-Focused Environment Setup 2018
2
How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow

Extra Help With Python Coding for Beginners (FAQ by Student Request)

1
How to Code by Yourself (part 1)
2
How to Code by Yourself (part 2)
3
Proof that using Jupyter Notebook is the same as not using it
4
Python 2 vs Python 3

Effective Learning Strategies for Machine Learning (FAQ by Student Request)

1
How to Succeed in this Course (Long Version)
2
Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
3
Machine Learning and AI Prerequisite Roadmap (pt 1)
4
Machine Learning and AI Prerequisite Roadmap (pt 2)

Appendix / FAQ Finale

1
What is the Appendix?
2
BONUS: Where to get discount coupons and FREE deep learning material
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