Data Science: Natural Language Processing (NLP) in Python

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

Register for this Course

$29.99 $199.99 USD 85% OFF!

Login or signup to register for this course

Have a coupon? Click here.

Course Data

Lectures: 80
Length: 10h 13m
Skill Level: All Levels
Languages: English
Includes: Lifetime access

Course 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 there are no mathematical prerequisites - 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.

Suggested Prerequisites:

  • calculus
  • linear algebra
  • probability
  • Python coding: if/else, loops, lists, dicts, sets
  • Numpy coding: matrix and vector operations, loading a CSV file
  • Sci-Kit Learn API, working knowledge of machine learning
  • Some familiarity with PCA, Markov Models, Logistic Regression


Natural Language Processing - What is it used for?

3 Lectures · 22min
  1. Introduction and Outline (07:49) (FREE preview available)
  2. Why Learn NLP? (05:59)
  3. The Central Message of this Course (08:12)

Course Preparation

4 Lectures · 20min
  1. How to Succeed in this Course (05:52)
  2. Where to get the code (09:21)
  3. Do you need a review of machine learning? (02:45)
  4. How to Open Files for Windows Users (02:18)

Decrypting Ciphers

13 Lectures · 01hr 32min
  1. Section Introduction (04:50)
  2. Ciphers (04:00)
  3. Language Models (16:07)
  4. Genetic Algorithms (21:24)
  5. Code Preparation (04:46)
  6. Code pt 1 (Notebook in Extras Section) (03:07)
  7. Code pt 2 (07:20)
  8. Code pt 3 (04:52)
  9. Code pt 4 (04:03)
  10. Code pt 5 (07:12)
  11. Code pt 6 (05:26)
  12. Section Conclusion (06:00)
  13. Suggestion Box (03:03)

Build your own spam detector

10 Lectures · 01hr 01min
  1. Build your own spam detector - description of data (02:09)
  2. Build your own spam detector using Naive Bayes and AdaBoost - the code (05:14)
  3. Key Takeaway from Spam Detection Exercise (05:57)
  4. Naive Bayes Concepts (09:57)
  5. AdaBoost Concepts (05:12)
  6. Other types of features (01:30)
  7. Spam Detection FAQ (Remedial #1) (08:45)
  8. What is a Vector? (Remedial #2) (06:05)
  9. SMS Spam Example (06:23)
  10. SMS Spam in Code (10:18)

Build your own sentiment analyzer

7 Lectures · 01hr 00min
  1. Description of Sentiment Analyzer (03:12)
  2. Logistic Regression Review (07:33)
  3. Preprocessing: Tokenization (04:48)
  4. Preprocessing: Tokens to Vectors (06:21)
  5. Sentiment Analysis in Python using Logistic Regression (19:48)
  6. Sentiment Analysis Extension (06:02)
  7. How to Improve Sentiment Analysis & FAQ (12:19)

NLTK Exploration

4 Lectures · 09min
  1. NLTK Exploration: POS Tagging (02:00)
  2. NLTK Exploration: Stemming and Lemmatization (02:06)
  3. NLTK Exploration: Named Entity Recognition (03:14)
  4. Want more NLTK? (02:00)

Latent Semantic Analysis

5 Lectures · 44min
  1. Latent Semantic Analysis - What does it do? (02:31)
  2. PCA and SVD - The underlying math behind LSA (15:49)
  3. Latent Semantic Analysis in Python (10:08)
  4. What is Latent Semantic Analysis Used For? (09:40)
  5. Extending LSA (06:17)

Write your own article spinner

6 Lectures · 37min
  1. Article Spinning Introduction and Markov Models (02:44)
  2. Trigram Model (02:12)
  3. More about Language Models (09:53)
  4. Precode Exercises (05:05)
  5. Writing an article spinner in Python (11:33)
  6. Article Spinner Extension Exercises (05:42)

How to learn more about NLP

1 Lectures · 02min
  1. What we didn't talk about (02:45)

Machine Learning Basics

11 Lectures · 01hr 34min
  1. Machine Learning: Section Introduction (07:47)
  2. What is Classification? (12:22)
  3. Classification in Code (14:39)
  4. What is Regression? (12:14)
  5. Regression in Code (08:30)
  6. What is a Feature Vector (06:49)
  7. Machine Learning is Nothing but Geometry (04:50)
  8. All Data is the Same (05:23)
  9. Comparing Different Machine Learning Models (09:47)
  10. Machine Learning and Deep Learning: Future Topics (05:55)
  11. Section Summary (05:47)

Natural Language Processing - What is it used for? (Legacy)

4 Lectures · 20min
  1. Introduction and Outline (07:49)
  2. NLP Applications (06:41)
  3. Why is NLP hard? (03:59)
  4. The Central Message of this Course (02:22)

Setting Up Your Environment (Appendix/FAQ by Student Request)

2 Lectures · 37min
  1. Windows-Focused Environment Setup 2018 (20:21)
  2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow (17:33)

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

4 Lectures · 42min
  1. How to Code Yourself (part 1) (15:55)
  2. How to Code Yourself (part 2) (09:24)
  3. Proof that using Jupyter Notebook is the same as not using it (12:29)
  4. Python 2 vs Python 3 (04:38)

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

4 Lectures · 59min
  1. How to Succeed in this Course (Long Version) (10:25)
  2. Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced? (22:05)
  3. What order should I take your courses in? (part 1) (11:19)
  4. What order should I take your courses in? (part 2) (16:07)

Appendix / FAQ Finale

2 Lectures · 08min
  1. What is the Appendix? (02:48)
  2. Where to get discount coupons and FREE deep learning material (05:31)


  • Cipher Decryption Colab Notebook
This website is using cookies. That's Fine