Data Science: Natural Language Processing (NLP) in Python from Udemy

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...

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)

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.

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Teaches theoretical algorithms such as Hidden Markov Models, Genetic Algorithms, and Naive Bayes that are used in the machine learning models of popular AI applications

Leverages free materials and coding in Python for assignments, which are accessible to those who may not have subscriptions or expensive software to purchase

Projects focus on building Markov models, genetic algorithms, sentiment analyzers, and an article spinner

Course is beginner-friendly, with tips on setting up a learning environment using Anaconda, installing necessary libraries, and coding independently

Prior machine learning knowledge is recommended, though not strictly required

Reviews summary

Build nlp algorithms from scratch

According to learners, this course offers a highly practical approach to Natural Language Processing, focusing heavily on building algorithms from scratch in Python. Many appreciate the instructor's emphasis on understanding the underlying mechanics rather than just using libraries, finding this method provides a solid foundation. Students often highlight the hands-on coding exercises and projects, such as building spam detectors and sentiment analyzers, as particularly engaging and useful. Some reviewers note the course is best suited for those with some prior Python and machine learning basics, as it can move quickly at times.

Provides a solid introduction to core NLP concepts.

"Gave me a really good introduction to the world of NLP and its applications."

"This course lays a great foundation for anyone wanting to dive deeper into NLP."

"I learned the basics of Markov models, Naive Bayes, and LSA effectively."

Lots of hands-on coding and project work.

"The hands-on coding and projects are the strongest part of the course for me."

"Building the spam detector and sentiment analyzer was practical and reinforced the concepts."

"If you learn best by coding along and doing projects, this course is for you."

Focuses on implementing NLP algorithms directly.

"The emphasis on implementing algorithms from scratch is unique and incredibly valuable."

"I finally understand what's going on under the hood, thanks to building everything step-by-step."

"Instead of just using libraries, we build the logic ourselves, which cemented my understanding."

Some reviewers note potential issues with code.

"Ran into some issues with library versions and had to tweak the code slightly."

"The code examples might need small adjustments depending on your environment setup."

"A few exercises required troubleshooting due to differences in package versions."

Best for learners with some ML/Python background.

"Beginners without prior ML knowledge might find some parts challenging."

"I recommend having a decent grasp of Python and basic machine learning before taking this."

"Assumes you are comfortable with Python and some basic statistics concepts."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Data Science: Natural Language Processing (NLP) in Python with these 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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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

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Build a Naive Bayes spam detector using a provided tutorial.

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Follow tutorial to build a spam detector
Test spam detector on a sample dataset
Improve spam detector by modifying the model

Sentiment Analysis Project

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Develop a sentiment analysis model on a real-world dataset.

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Collect and prepare a sentiment analysis dataset
Train and evaluate a sentiment analysis model
Evaluate the model's performance

Deploy NLP Model

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Learn to deploy an NLP model using a cloud platform.

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NLP Research Project

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In-depth NLP project covering model development and evaluation.

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Career center

Learners who complete Data Science: Natural Language Processing (NLP) in Python will develop knowledge and skills that may be useful to these careers:

NLP Engineer

NLP Engineers build and maintain natural language processing (NLP) systems. NLP systems allow computers to understand and generate human language. This course provides a strong foundation in NLP concepts and teaches you how to apply these concepts to real-world problems. You will learn how to build and train NLP models using popular programming languages and tools, and you will gain experience with a variety of NLP applications, including machine translation, text classification, and speech recognition.

See salaries and explore the career path for NLP Engineer

Machine Learning Engineer

Machine Learning Engineers create and implement machine learning algorithms to solve complex problems. This course provides a strong foundation in machine learning concepts (such as probability, linear algebra, and optimization) and teaches you how to apply these concepts to real-world problems. You will learn how to build and train machine learning models using popular programming languages and tools, and you will gain experience with a variety of machine learning algorithms, including supervised learning, unsupervised learning, and deep learning.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists use data to solve business problems. They clean, analyze, and interpret data to extract insights and make predictions. This course provides a strong foundation in data science concepts and teaches you how to apply these concepts to real-world problems. You will learn how to use popular data science tools and techniques, and you will gain experience with a variety of data science projects.

See salaries and explore the career path for Data Scientist

Data Analyst

Data Analysts clean, analyze, and interpret data to find trends and patterns. They use this information to make informed decisions and solve business problems. This course provides a strong foundation in data analysis concepts and teaches you how to apply these concepts to real-world problems. You will learn how to use popular data analysis tools and techniques, and you will gain experience with a variety of data analysis projects.

See salaries and explore the career path for Data Analyst

Software Engineer

Software Engineers design, develop, and maintain software applications. This course provides a strong foundation in software engineering concepts and teaches you how to apply these concepts to real-world problems. You will learn how to use popular programming languages and tools, and you will gain experience with a variety of software engineering projects.

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Product Manager

Product Managers are responsible for the development and launch of new products. They work with engineers, designers, and marketing teams to bring new products to market. This course provides a strong foundation in product management concepts and teaches you how to apply these concepts to real-world problems. You will learn how to conduct market research, develop product roadmaps, and launch new products.

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Marketing Manager

Marketing Managers are responsible for developing and executing marketing campaigns. They work with product teams, sales teams, and customer service teams to promote new products and services. This course provides a strong foundation in marketing concepts and teaches you how to apply these concepts to real-world problems. You will learn how to conduct market research, develop marketing plans, and execute marketing campaigns.

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Sales Manager

Sales Managers are responsible for leading and managing sales teams. They work with sales representatives to develop and execute sales strategies. This course provides a strong foundation in sales concepts and teaches you how to apply these concepts to real-world problems. You will learn how to develop sales plans, motivate sales teams, and close deals.

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Customer Success Manager

Customer Success Managers are responsible for ensuring that customers are happy with their products and services. They work with customers to resolve issues, provide training, and build relationships. This course provides a strong foundation in customer service concepts and teaches you how to apply these concepts to real-world problems. You will learn how to resolve customer issues, provide training, and build relationships.

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Technical Writer

Technical Writers create and maintain technical documentation. They work with engineers, scientists, and other professionals to document complex technical information. This course provides a strong foundation in technical writing concepts and teaches you how to apply these concepts to real-world problems. You will learn how to write clear and concise technical documentation.

See salaries and explore the career path for Technical Writer

UX Designer

UX Designers design the user experience for websites and applications. They work with product teams, engineers, and marketing teams to create user-friendly and engaging experiences. This course provides a strong foundation in UX design concepts and teaches you how to apply these concepts to real-world problems. You will learn how to conduct user research, design user interfaces, and test user experiences.

See salaries and explore the career path for UX Designer

UI Designer

UI Designers design the visual appearance of websites and applications. They work with UX designers, engineers, and marketing teams to create user-friendly and engaging experiences. This course provides a strong foundation in UI design concepts and teaches you how to apply these concepts to real-world problems. You will learn how to use design tools, create prototypes, and design user interfaces.

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Front-End Developer

Front-End Developers develop the front-end of websites and applications. They work with UX designers, UI designers, and back-end developers to create user-friendly and engaging experiences. This course provides a strong foundation in front-end development concepts and teaches you how to apply these concepts to real-world problems. You will learn how to use HTML, CSS, and JavaScript to create front-end applications.

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Back-End Developer

Back-End Developers develop the back-end of websites and applications. They work with front-end developers, UX designers, and UI designers to create user-friendly and engaging experiences. This course provides a strong foundation in back-end development concepts and teaches you how to apply these concepts to real-world problems. You will learn how to use programming languages such as Python, Java, and C++ to create back-end applications.

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Full-Stack Developer

Full-Stack Developers develop both the front-end and back-end of websites and applications. They work with UX designers, UI designers, and other developers to create user-friendly and engaging experiences. This course provides a strong foundation in full-stack development concepts and teaches you how to apply these concepts to real-world problems. You will learn how to use a variety of programming languages and tools to create full-stack applications.

See salaries and explore the career path for Full-Stack Developer