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Development Data Science Python

Data Science: Natural Language Processing (NLP) in Python

Applications: decrypting ciphers, spam detection, sentiment analysis, article spinners, and latent semantic analysis.
Rating: 4.4 out of 54.4 (9,229 ratings)
36,659 students
Created by Lazy Programmer Inc.
Last updated 2/2021
English
English [Auto], German [Auto], 
30-Day Money-Back Guarantee

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

Course content

14 sections • 76 lectures • 10h 2m total length

  • Preview07:48
  • Why Learn NLP?
    05:59
  • The Central Message of this Course (Big Picture Perspective)
    08:12

  • Anyone Can Succeed in this Course
    12:42
  • Where to get the code and data
    05:01
  • How to Open Files for Windows Users
    02:18

  • Machine Learning: Section Introduction
    16:07
  • What is Classification?
    12:22
  • Classification in Code
    14:38
  • What is Regression?
    12:13
  • Regression in Code
    08:29
  • What is a Feature Vector?
    06:48
  • Machine Learning is Nothing but Geometry
    04:50
  • All Data is the Same
    05:23
  • Comparing Different Machine Learning Models
    09:46
  • Machine Learning and Deep Learning: Future Topics
    05:55
  • Section Summary
    05:47

  • Section Introduction
    07:11
  • Preview03:59
  • Language Models
    16:06
  • Genetic Algorithms
    21:23
  • Code Preparation
    04:46
  • Link to Cipher Notebook
    00:00
  • Code pt 1
    03:06
  • Code pt 2
    07:20
  • Code pt 3
    04:52
  • Code pt 4
    04:03
  • Code pt 5
    07:12
  • Code pt 6
    05:25
  • Section Conclusion
    06:00

  • Build your own spam detector - description of data
    02:08
  • Build your own spam detector using Naive Bayes and AdaBoost - the code
    05:14
  • Key Takeaway from Spam Detection Exercise
    05:56
  • Naive Bayes Concepts
    09:56
  • AdaBoost Concepts
    05:11
  • Other types of features
    01:30
  • Spam Detection FAQ (Remedial #1)
    08:45
  • What is a Vector? (Remedial #2)
    06:04
  • SMS Spam Example
    06:23
  • SMS Spam in Code
    10:43
  • Suggestion Box
    03:03

  • Description of Sentiment Analyzer
    03:12
  • Logistic Regression Review
    07:32
  • Preprocessing: Tokenization
    04:48
  • Preprocessing: Tokens to Vectors
    06:20
  • Sentiment Analysis in Python using Logistic Regression
    19:48
  • Sentiment Analysis Extension
    06:01
  • How to Improve Sentiment Analysis & FAQ
    12:19

  • NLTK Exploration: POS Tagging
    02:00
  • NLTK Exploration: Stemming and Lemmatization
    02:06
  • NLTK Exploration: Named Entity Recognition
    03:13
  • Want more NLTK?
    01:59

  • Latent Semantic Analysis - What does it do?
    02:30
  • SVD - The underlying math behind LSA
    15:49
  • Latent Semantic Analysis in Python
    10:08
  • What is Latent Semantic Analysis Used For?
    09:40
  • Extending LSA
    06:16

  • Article Spinning Introduction and Markov Models
    02:43
  • Trigram Model
    02:11
  • More about Language Models
    09:53
  • Precode Exercises
    05:05
  • Writing an article spinner in Python
    11:33
  • Article Spinner Extension Exercises
    05:42

  • What we didn't talk about
    02:45

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 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)

Who this course is for:

  • 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.

Featured review

Andrea Clark-Sevilla
Andrea Clark-Sevilla
49 courses
9 reviews
Rating: 5.0 out of 5a year ago
I really enjoyed this course! It was extremely useful for solidifying my understanding of many NLP-applied, machine learning algorithms that I was exposed to previously but did not actually implement myself. The professor is top-notch (his teaching approach is superb) and very helpful in the Q&A section! I look forward to completing more of his courses.

Instructor

Lazy Programmer Inc.
Artificial intelligence and machine learning engineer
Lazy Programmer Inc.
  • 4.6 Instructor Rating
  • 108,419 Reviews
  • 423,004 Students
  • 28 Courses

Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer.

I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition.

Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark.

I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text.

My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing.

I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. 

Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more.

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