Data Science: Natural Language Processing (Nlp) In Python

Applications: decrypting ciphers, spam detection, sentiment analysis, article spinners, and latent semantic analysis.

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 scratchOther 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…

Overview

Section 1: Natural Language Processing - What is it used for?

Lecture 1 Introduction and Outline

Lecture 2 Why Learn NLP?

Lecture 3 The Central Message of this Course (Big Picture Perspective)

Section 2: Course Preparation

Lecture 4 How to Succeed in this Course

Lecture 5 Where to get the code and data

Lecture 6 How to Open Files for Windows Users

Section 3: Machine Learning Basics Review

Lecture 7 Machine Learning: Section Introduction

Lecture 8 What is Classification?

Lecture 9 Classification in Code

Lecture 10 What is Regression?

Lecture 11 Regression in Code

Lecture 12 What is a Feature Vector?

Lecture 13 Machine Learning is Nothing but Geometry

Lecture 14 All Data is the Same

Lecture 15 Comparing Different Machine Learning Models

Lecture 16 Machine Learning and Deep Learning: Future Topics

Lecture 17 Section Summary

Section 4: Markov Models

Lecture 18 Markov Models Section Introduction

Lecture 19 The Markov Property

Lecture 20 The Markov Model

Lecture 21 Probability Smoothing and Log-Probabilities

Lecture 22 Building a Text Classifier (Theory)

Lecture 23 Building a Text Classifier (Exercise Prompt)

Lecture 24 Building a Text Classifier (Code pt 1)

Lecture 25 Building a Text Classifier (Code pt 2)

Lecture 26 Language Model (Theory)

Lecture 27 Language Model (Exercise Prompt)

Lecture 28 Language Model (Code pt 1)

Lecture 29 Language Model (Code pt 2)

Lecture 30 Markov Models Section Summary

Section 5: Decrypting Ciphers

Lecture 31 Section Introduction

Lecture 32 Ciphers

Lecture 33 Language Models

Lecture 34 Genetic Algorithms

Lecture 35 Code Preparation

Lecture 36 Code pt 1

Lecture 37 Code pt 2

Lecture 38 Code pt 3

Lecture 39 Code pt 4

Lecture 40 Code pt 5

Lecture 41 Code pt 6

Lecture 42 Section Conclusion

Section 6: Build your own spam detector

Lecture 43 Build your own spam detector - description of data

Lecture 44 Build your own spam detector using Naive Bayes and AdaBoost - the code

Lecture 45 Key Takeaway from Spam Detection Exercise

Lecture 46 Naive Bayes Concepts

Lecture 47 AdaBoost Concepts

Lecture 48 Other types of features

Lecture 49 Spam Detection FAQ (Remedial #1)

Lecture 50 What is a Vector? (Remedial #2)

Lecture 51 SMS Spam Example

Lecture 52 SMS Spam in Code

Lecture 53 Suggestion Box

Section 7: Build your own sentiment analyzer

Lecture 54 Description of Sentiment Analyzer

Lecture 55 Logistic Regression Review

Lecture 56 Preprocessing: Tokenization

Lecture 57 Preprocessing: Tokens to Vectors

Lecture 58 Sentiment Analysis in Python using Logistic Regression

Lecture 59 Sentiment Analysis Extension

Lecture 60 How to Improve Sentiment Analysis & FAQ

Section 8: NLTK Exploration

Lecture 61 NLTK Exploration: POS Tagging

Lecture 62 NLTK Exploration: Stemming and Lemmatization

Lecture 63 NLTK Exploration: Named Entity Recognition

Lecture 64 Want more NLTK?

Section 9: Latent Semantic Analysis

Lecture 65 Latent Semantic Analysis - What does it do?

Lecture 66 SVD - The underlying math behind LSA

Lecture 67 Latent Semantic Analysis in Python

Lecture 68 What is Latent Semantic Analysis Used For?

Lecture 69 Extending LSA

Section 10: Write your own article spinner

Lecture 70 Article Spinning Introduction and Markov Models

Lecture 71 Trigram Model

Lecture 72 More about Language Models

Lecture 73 Precode Exercises

Lecture 74 Writing an article spinner in Python

Lecture 75 Article Spinner Extension Exercises

Section 11: How to learn more about NLP

Lecture 76 What we didn't talk about

Section 12: Setting Up Your Environment (FAQ by Student Request)

Lecture 77 Anaconda Environment Setup

Lecture 78 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow

Section 13: Extra Help With Python Coding for Beginners (FAQ by Student Request)

Lecture 79 How to Code by Yourself (part 1)

Lecture 80 How to Code by Yourself (part 2)

Lecture 81 Proof that using Jupyter Notebook is the same as not using it

Lecture 82 Python 2 vs Python 3

Section 14: Effective Learning Strategies for Machine Learning (FAQ by Student Request)

Lecture 83 How to Succeed in this Course (Long Version)

Lecture 84 Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?

Lecture 85 Machine Learning and AI Prerequisite Roadmap (pt 1)

Lecture 86 Machine Learning and AI Prerequisite Roadmap (pt 2)

Section 15: Appendix / FAQ Finale

Lecture 87 What is the Appendix?

Lecture 88 BONUS

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.