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Data Science: Natural Language Processing (NLP) in Python

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Data Science: Natural Language Processing (NLP) in Python, available at $109.99, has an average rating of 4.73, with 95 lectures, based on 12631 reviews, and has 48796 subscribers.

You will learn about 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 Understand important foundations for OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion This course is ideal for individuals who are Students who are comfortable writing Python code, using loops, lists, dictionaries, etc. or Students who want to learn more about machine learning but don't want to do a lot of math or Professionals who are interested in applying machine learning and NLP to practical problems like spam detection, Internet marketing, and sentiment analysis or This course is NOT for those who find the tasks and methods listed in the curriculum too basic. or 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). or 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. It is particularly useful for Students who are comfortable writing Python code, using loops, lists, dictionaries, etc. or Students who want to learn more about machine learning but don't want to do a lot of math or Professionals who are interested in applying machine learning and NLP to practical problems like spam detection, Internet marketing, and sentiment analysis or This course is NOT for those who find the tasks and methods listed in the curriculum too basic. or 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). or 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.

Enroll now: Data Science: Natural Language Processing (NLP) in Python

Summary

Title: Data Science: Natural Language Processing (NLP) in Python

Price: $109.99

Average Rating: 4.73

Number of Lectures: 95

Number of Published Lectures: 90

Number of Curriculum Items: 95

Number of Published Curriculum Objects: 90

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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
  • Understand important foundations for OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion

Who Should Attend

  • 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.

Target Audiences

  • 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.

Ever wondered how AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion really work? In this course, you will learn the foundations of these groundbreaking applications.

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 analysisin 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…

Course Curriculum

Chapter 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)

Chapter 2: Course Preparation

Lecture 1: How to Succeed in this Course

Lecture 2: Where to get the code and data

Lecture 3: How to Open Files for Windows Users

Chapter 3: Machine Learning Basics Review

Lecture 1: Machine Learning: Section Introduction

Lecture 2: What is Classification?

Lecture 3: Classification in Code

Lecture 4: What is Regression?

Lecture 5: Regression in Code

Lecture 6: What is a Feature Vector?

Lecture 7: Machine Learning is Nothing but Geometry

Lecture 8: All Data is the Same

Lecture 9: Comparing Different Machine Learning Models

Lecture 10: Machine Learning and Deep Learning: Future Topics

Lecture 11: Section Summary

Chapter 4: Markov Models

Lecture 1: Markov Models Section Introduction

Lecture 2: The Markov Property

Lecture 3: The Markov Model

Lecture 4: Probability Smoothing and Log-Probabilities

Lecture 5: Building a Text Classifier (Theory)

Lecture 6: Building a Text Classifier (Exercise Prompt)

Lecture 7: Building a Text Classifier (Code pt 1)

Lecture 8: Building a Text Classifier (Code pt 2)

Lecture 9: Language Model (Theory)

Lecture 10: Language Model (Exercise Prompt)

Lecture 11: Language Model (Code pt 1)

Lecture 12: Language Model (Code pt 2)

Lecture 13: Markov Models Section Summary

Chapter 5: Decrypting Ciphers

Lecture 1: Section Introduction

Lecture 2: Ciphers

Lecture 3: Language Models

Lecture 4: Genetic Algorithms

Lecture 5: Code Preparation

Lecture 6: Code pt 1

Lecture 7: Code pt 2

Lecture 8: Code pt 3

Lecture 9: Code pt 4

Lecture 10: Code pt 5

Lecture 11: Code pt 6

Lecture 12: Real-World Application: Acoustic Keylogger

Lecture 13: Section Conclusion

Chapter 6: Build your own spam detector

Lecture 1: Build your own spam detector – description of data

Lecture 2: Build your own spam detector using Naive Bayes and AdaBoost – the code

Lecture 3: Key Takeaway from Spam Detection Exercise

Lecture 4: Naive Bayes Concepts

Lecture 5: AdaBoost Concepts

Lecture 6: Other types of features

Lecture 7: Spam Detection FAQ (Remedial #1)

Lecture 8: What is a Vector? (Remedial #2)

Lecture 9: SMS Spam Example

Lecture 10: SMS Spam in Code

Lecture 11: Suggestion Box

Chapter 7: Build your own sentiment analyzer

Lecture 1: Description of Sentiment Analyzer

Lecture 2: Logistic Regression Review

Lecture 3: Preprocessing: Tokenization

Lecture 4: Preprocessing: Tokens to Vectors

Lecture 5: Sentiment Analysis in Python using Logistic Regression

Lecture 6: Sentiment Analysis Extension

Lecture 7: How to Improve Sentiment Analysis & FAQ

Chapter 8: NLTK Exploration

Lecture 1: NLTK Exploration: POS Tagging

Lecture 2: NLTK Exploration: Stemming and Lemmatization

Lecture 3: NLTK Exploration: Named Entity Recognition

Lecture 4: Want more NLTK?

Chapter 9: Latent Semantic Analysis

Lecture 1: Latent Semantic Analysis – What does it do?

Lecture 2: SVD – The underlying math behind LSA

Lecture 3: Latent Semantic Analysis in Python

Lecture 4: What is Latent Semantic Analysis Used For?

Lecture 5: Extending LSA

Chapter 10: Write your own article spinner

Lecture 1: Article Spinning Introduction and Markov Models

Lecture 2: Trigram Model

Lecture 3: More about Language Models

Lecture 4: Precode Exercises

Lecture 5: Writing an article spinner in Python

Lecture 6: Article Spinner Extension Exercises

Chapter 11: How to learn more about NLP

Lecture 1: What we didn't talk about

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

Lecture 1: Pre-Installation Check

Lecture 2: Anaconda Environment Setup

Lecture 3: How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow

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

Lecture 1: How to Code by Yourself (part 1)

Lecture 2: How to Code by Yourself (part 2)

Lecture 3: Proof that using Jupyter Notebook is the same as not using it

Lecture 4: Python 2 vs Python 3

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

Lecture 1: How to Succeed in this Course (Long Version)

Lecture 2: Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?

Instructors

Rating Distribution

  • 1 stars: 130 votes
  • 2 stars: 180 votes
  • 3 stars: 710 votes
  • 4 stars: 4386 votes
  • 5 stars: 7225 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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