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.
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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.
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 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...
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What's inside
Learning objectives
- 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
- 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
Syllabus
Know about NLP applications and some tasks it performs.
Introduction and Outline
Why Learn NLP?
The Central Message of this Course (Big Picture Perspective)
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Syllabus
In addition to the word frequencies we looked at previously, this lecture looks at bag-of-words in general and different ways to implement that, including raw counts and binary indicator variables. We also briefly mention TF-IDF.
What is sentiment analysis? In this lecture we'll look at the data we'll be using to build our sentiment analysis tool, and talk about how we can manually pre-process the data so that we can plug it into a machine learning classifier.
In this lecture we'll write our sentiment analyzer in Python to predict sentiment on Amazon reviews.
How do we tag the tokens of a sentence by their parts-of-speech? i.e. Is this token a noun, verb, adjective, adverb, or something else?
How do we turn words into their "base form"? i.e. plural to singular
Can we also tag parts of a sentence as a "person", "organization", or "location"?
What is synonymy and polysemy and how can LSA / LSI help?
What is article spinning and how is it related to search engines, SEO (search engine optimization), and Internet marketing?
What will be our strategy for creating an article spinner? We'll create a trigram model.
Important NLP topics you should "know of" but that we didn't cover, and where you can learn more.
Good to know
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Reviews summary
Concise, well-paced nlp course
Materials presented in a structured way.
Instructor is very knowledgeable in the subject matter.
Course is well-paced.
Activities
Python Review
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Refresh your Python skills in preparation for the course.
Browse courses on
Python
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NLP with Python
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Provides a comprehensive introduction to the fundamentals of NLP
View
Natural Language Processing with Python:...
on Amazon
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NLP Study Group
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Join a study group to discuss course concepts with peers.
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Five other activities
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Show all eight activities
Write Markov Model in Python
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Write a Python implementation of a Markov model for text generation.
Browse courses on
Markov Models
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-
Review Markov Models theory
-
Implement Markov Model in Python
-
Test Markov Model on a sample text
Build Spam Detector
Show steps
Build a Naive Bayes spam detector using a provided tutorial.
Show steps
-
Follow tutorial to build a spam detector
-
Test spam detector on a sample dataset
-
Improve spam detector by modifying the model
Sentiment Analysis Project
Show steps
Develop a sentiment analysis model on a real-world dataset.
Show steps
-
Collect and prepare a sentiment analysis dataset
-
Train and evaluate a sentiment analysis model
-
Evaluate the model's performance
Deploy NLP Model
Show steps
Learn to deploy an NLP model using a cloud platform.
Show steps
NLP Research Project
Show steps
In-depth NLP project covering model development and evaluation.
Show steps
Python Review
Show steps
Refresh your Python skills in preparation for the course.
Browse courses on
Python
Show steps
NLP with Python
Show steps
Provides a comprehensive introduction to the fundamentals of NLP
View
Natural Language Processing with Python:...
on Amazon
Show steps
NLP Study Group
Show steps
Join a study group to discuss course concepts with peers.
Show steps
Write Markov Model in Python
Show steps
Write a Python implementation of a Markov model for text generation.
Browse courses on
Markov Models
Show steps
- Review Markov Models theory
- Implement Markov Model in Python
- Test Markov Model on a sample text
Build Spam Detector
Show steps
Build a Naive Bayes spam detector using a provided tutorial.
Show steps
- Follow tutorial to build a spam detector
- Test spam detector on a sample dataset
- Improve spam detector by modifying the model
Sentiment Analysis Project
Show steps
Develop a sentiment analysis model on a real-world dataset.
Show steps
- Collect and prepare a sentiment analysis dataset
- Train and evaluate a sentiment analysis model
- Evaluate the model's performance
Deploy NLP Model
Show steps
Learn to deploy an NLP model using a cloud platform.
Show steps
NLP Research Project
Show steps
In-depth NLP project covering model development and evaluation.
Show steps
Career center
NLP Engineer
Machine Learning Engineer
Data Scientist
Data Analyst
Software Engineer
Product Manager
Marketing Manager
Sales Manager
Customer Success Manager
Technical Writer
UX Designer
UI Designer
Front-End Developer
Back-End Developer
Full-Stack Developer
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