Data Science: Natural Language Processing (NLP) in Python

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

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Course Data

Lectures: 95
Length: 12h 12m
Skill Level: All Levels
Languages: English
Includes: Lifetime access, certificate of completion (shareable on LinkedIn, Facebook, and Twitter), Q&A forum

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

Testimonials and Success Stories

I am one of your students. Yesterday, I presented my paper at ICCV 2019. You have a significant part in this, so I want to sincerely thank you for your in-depth guidance to the puzzle of deep learning. Please keep making awesome courses that teach us!

I just watched your short video on “Predicting Stock Prices with LSTMs: One Mistake Everyone Makes.” Giggled with delight.

You probably already know this, but some of us really and truly appreciate you. BTW, I spent a reasonable amount of time making a learning roadmap based on your courses and have started the journey.

Looking forward to your new stuff.

Thank you for doing this! I wish everyone who call’s themselves a Data Scientist would take the time to do this either as a refresher or learn the material. I have had to work with so many people in prior roles that wanted to jump right into machine learning on my teams and didn’t even understand the first thing about the basics you have in here!!

I am signing up so that I have the easy refresh when needed and the see what you consider important, as well as to support your great work, thank you.

Thank you, I think you have opened my eyes. I was using API to implement Deep learning algorithms and each time I felt I was messing out on some things. So thank you very much.

I have been intending to send you an email expressing my gratitude for the work that you have done to create all of these data science courses in Machine Learning and Artificial Intelligence. I have been looking long and hard for courses that have mathematical rigor relative to the application of the ML & AI algorithms as opposed to just exhibit some 'canned routine' and then viola here is your neural network or logistical regression. ...


I have now taken a few classes from some well-known AI profs at Stanford (Andrew Ng, Christopher Manning, …) with an overall average mark in the mid-90s. Just so you know, you are as good as any of them. But I hope that you already know that.

I wish you a happy and safe holiday season. I am glad you chose to share your knowledge with the rest of us.

Hi Sir I am a student from India. I've been wanting to write a note to thank you for the courses that you've made because they have changed my career. I wanted to work in the field of data science but I was not having proper guidance but then I stumbled upon your "Logistic Regression" course in March and since then, there's been no looking back. I learned ANNs, CNNs, RNNs, Tensorflow, NLP and whatnot by going through your lectures. The knowledge that I gained enabled me to get a job as a Business Technology Analyst at one of my dream firms even in the midst of this pandemic. For that, I shall always be grateful to you. Please keep making more courses with the level of detail that you do in low-level libraries like Theano.

I just wanted to reach out and thank you for your most excellent course that I am nearing finishing.

And, I couldn't agree more with some of your "rants", and found myself nodding vigorously!

You are an excellent teacher, and a rare breed.

And, your courses are frankly, more digestible and teach a student far more than some of the top-tier courses from ivy leagues I have taken in the past.

(I plan to go through many more courses, one by one!)

I know you must be deluged with complaints in spite of the best content around That's just human nature.

Also, satisfied people rarely take the time to write, so I thought I will write in for a change. :)

Hello, Lazy Programmer!

In the process of completing my Master’s at Hunan University, China, I am writing this feedback to you in order to express my deep gratitude for all the knowledge and skills I have obtained studying your courses and following your recommendations.

The first course of yours I took was on Convolutional Neural Networks (“Deep Learning p.5”, as far as I remember). Answering one of my questions on the Q&A board, you suggested I should start from the beginning – the Linear and Logistic Regression courses. Despite that I assumed I had already known many basic things at that time, I overcame my “pride” and decided to start my journey in Deep Learning from scratch. ...


By the way, if you are interested to hear. I used the HMM classification, as it was in your course (95% of the script, I had little adjustments there), for the Customer-Care department in a big known fintech company. to predict who will call them, so they can call him before the rush hours, and improve the service. Instead of a poem, I Had a sequence of the last 24 hours' events that the customer had, like: "Loaded money", "Usage in the food service", "Entering the app", "Trying to change the password", etc... the label was called or didn't call. The outcome was great. They use it for their VIP customers. Our data science department and I got a lot of praise.


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 · 17min
  1. How to Succeed in this Course (03:04)
  2. Where to get the code (09:21)
  3. Do you need a review of machine learning? (02:46)
  4. How to Open Files for Windows Users (02:18)

Markov Models

13 Lectures · 01hr 47min
  1. Markov Models Section Introduction (02:43)
  2. The Markov Property (07:35)
  3. The Markov Model (12:31)
  4. Probability Smoothing and Log-Probabilities (07:51)
  5. Building a Text Classifier (Theory) (07:30)
  6. Building a Text Classifier (Exercise Prompt) (06:34)
  7. Building a Text Classifier (Code pt 1) (10:33)
  8. Building a Text Classifier (Code pt 2) (12:07)
  9. Language Model (Theory) (10:16)
  10. Language Model (Exercise Prompt) (06:53)
  11. Language Model (Code pt 1) (10:45)
  12. Language Model (Code pt 2) (09:26)
  13. Markov Models Section Summary (03:01)

Decrypting Ciphers

14 Lectures · 01hr 37min
  1. Section Introduction (07:12)
  2. Ciphers (04:00)
  3. Language Models (16:07)
  4. Genetic Algorithms (21:24)
  5. Code Preparation (04:47)
  6. Code pt 1 (Notebook in Extras Section) (03:07)
  7. Code pt 2 (07:21)
  8. Code pt 3 (04:53)
  9. Code pt 4 (04:04)
  10. Code pt 5 (07:12)
  11. Code pt 6 (05:26)
  12. Cipher Decryption - Additional Discussion (02:57)
  13. Section Conclusion (06:01)
  14. Suggestion Box (03:10)

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 (09:56)
  5. AdaBoost Concepts (05:12)
  6. Other types of features (01:31)
  7. Spam Detection FAQ (Remedial #1) (08:45)
  8. What is a Vector? (Remedial #2) (06:05)
  9. SMS Spam Example (06:24)
  10. SMS Spam in Code (10:18)

Build your own sentiment analyzer

7 Lectures · 01hr 00min
  1. Description of Sentiment Analyzer (03:13)
  2. Logistic Regression Review (07:33)
  3. Preprocessing: Tokenization (04:49)
  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 (12:20)

NLTK Exploration

4 Lectures · 09min
  1. NLTK Exploration: POS Tagging (02:01)
  2. NLTK Exploration: Stemming and Lemmatization (02:07)
  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 (15:50)
  3. Latent Semantic Analysis in Python (10:08)
  4. What is Latent Semantic Analysis Used For? (09:41)
  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 (02:13)
  3. More about Language Models (09:54)
  4. Precode Exercises (05:05)
  5. Writing an article spinner in Python (11:33)
  6. Article Spinner Extension Exercises (05:43)

How to learn more about NLP

1 Lectures · 02min
  1. What we didn't talk about (02:46)

Machine Learning Basics

11 Lectures · 01hr 42min
  1. Machine Learning: Section Introduction (16:08)
  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 · 16min
  1. Introduction and Outline (03:05)
  2. NLP Applications (06:41)
  3. Why is NLP hard? (04:00)
  4. The Central Message of this Course (02:23)

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

3 Lectures · 42min
  1. Pre-Installation Check (04:13)
  2. Anaconda Environment Setup (20:21)
  3. 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 (05:49)


  • Cipher Decryption Colab Notebook
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