Data Science: Natural Language Processing (NLP) in Python

Data Science: Natural Language Processing (NLP) in Python
Data Science: Natural Language Processing (NLP) in Python
English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 8 Hours | 1.64 GB

Practical Applications of NLP: spam detection, sentiment analysis, article spinners, and latent semantic analysis.

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 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.

What you’ll learn

  • 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
Table of Contents

Natural Language Processing – What is it used for
1 Introduction and Outline
2 NLP Applications
3 Why is NLP hard
4 The Central Message of this Course

Course Preparation
5 How to Succeed in this Course
6 Where to get the code and data
7 Do you need a review of machine learning

Build your own spam detector
8 Build your own spam detector – description of data
9 SMS Spam in Code
10 Build your own spam detector using Naive Bayes and AdaBoost – the code
11 Key Takeaway from Spam Detection Exercise
12 Naive Bayes Concepts
13 AdaBoost Concepts
14 Other types of features
15 Spam Detection FAQ (Remedial #1)
16 What is a Vector (Remedial #2)
17 SMS Spam Example

Build your own sentiment analyzer
18 Description of Sentiment Analyzer
19 Logistic Regression Review
20 Preprocessing Tokenization
21 Preprocessing Tokens to Vectors
22 Sentiment Analysis in Python using Logistic Regression
23 Sentiment Analysis Extension
24 How to Improve Sentiment Analysis & FAQ

NLTK Exploration
25 NLTK Exploration POS Tagging
26 NLTK Exploration Stemming and Lemmatization
27 NLTK Exploration Named Entity Recognition
28 Want more NLTK

Latent Semantic Analysis
29 Latent Semantic Analysis – What does it do
30 SVD – The underlying math behind LSA
31 Latent Semantic Analysis in Python
32 What is Latent Semantic Analysis Used For
33 Extending LSA

Write your own article spinner
34 Article Spinning Introduction and Markov Models
35 More about Language Models
36 Trigram Model
37 Precode Exercises
38 Writing an article spinner in Python
39 Article Spinner Extension Exercises

How to learn more about NLP
40 What we didn’t talk about

Machine Learning Basics Review
41 (Review) Machine Learning Section Introduction
42 (Review) Machine Learning and Deep Learning Future Topics
43 (Review) Section Summary
44 (Review) What is Classification
45 (Review) Classification in Code
46 (Review) What is Regression
47 (Review) Regression in Code
48 (Review) What is a Feature Vector
49 (Review) Machine Learning is Nothing but Geometry
50 (Review) All Data is the Same
51 (Review) Comparing Different Machine Learning Models

52 What is the Appendix
53 What order should I take your courses in (part 1)
54 What order should I take your courses in (part 2)
55 Windows-Focused Environment Setup 2018
56 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
57 How to Code by Yourself (part 1)
58 How to Code by Yourself (part 2)
59 How to Succeed in this Course (Long Version)
60 Is this for Beginners or Experts Academic or Practical Fast or slow-paced
61 Proof that using Jupyter Notebook is the same as not using it
62 Python 2 vs Python 3