Natural Language Processing with Deep Learning 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 we are going to look at NLP (natural language processing) with deep learning.

Previously, you learned about some of the basics, like how many NLP problems are just regular machine learning and data science problems in disguise, and simple, practical methods like bag-of-words and term-document matrices.

These allowed us to do some pretty cool things, like detect spam emails, write poetry, spin articles, and group together similar words.

In this course I’m going to show you how to do even more awesome things. We’ll learn not just 1, but 4 new architectures in this course.

First up is word2vec.

In this course, I’m going to show you exactly how word2vec works, from theory to implementation, and you’ll see that it’s merely the application of skills you already know.

Word2vec is interesting because it magically maps words to a vector space where you can find analogies, like:

king - man = queen - woman
France - Paris = England - London
December - Novemeber = July - June

For those beginners who find algorithms tough and just want to use a library, we will demonstrate the use of the Gensim library to obtain pre-trained word vectors, compute similarities and analogies, and apply those word vectors to build text classifiers.

We are also going to look at the GloVe method, which also finds word vectors, but uses a technique called matrix factorization, which is a popular algorithm for recommender systems.

Amazingly, the word vectors produced by GLoVe are just as good as the ones produced by word2vec, and it’s way easier to train.

We will also look at some classical NLP problems, like parts-of-speech tagging and named entity recognition, and use recurrent neural networks to solve them. You’ll see that just about any problem can be solved using neural networks, but you’ll also learn the dangers of having too much complexity.

Lastly, you’ll learn about recursive neural networks, which finally help us solve the problem of negation in sentiment analysis. Recursive neural networks exploit the fact that sentences have a tree structure, and we can finally get away from naively using bag-of-words.

All of the materials required for this course can be downloaded and installed for FREE. We will do most of our work in Numpy, Matplotlib, and Theano. I am always available to answer your questions and help you along your data science journey.

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.

See you in class.

"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:

calculus (taking derivatives)
matrix addition, multiplication
probability (conditional and joint distributions)
Python coding: if/else, loops, lists, dicts, sets
Numpy coding: matrix and vector operations, loading a CSV file
neural networks and backpropagation, be able to derive and code gradient descent algorithms on your own
Can write a feedforward neural network in Theano or TensorFlow
Can write a recurrent neural network / LSTM / GRU in Theano or TensorFlow from basic primitives, especially the scan function
Helpful to have experience with tree algorithms

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)

UNIQUE FEATURES

Every line of code explained in detail - email me any time if you disagree
No wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratch
Not afraid of university-level math - get important details about algorithms that other courses leave out

What's inside

Learning objectives

Understand and implement word2vec
Understand the cbow method in word2vec
Understand the skip-gram method in word2vec
Understand the negative sampling optimization in word2vec
Understand and implement glove using gradient descent and alternating least squares
Use recurrent neural networks for parts-of-speech tagging
Use recurrent neural networks for named entity recognition

Understand and implement recursive neural networks for sentiment analysis
Understand and implement recursive neural tensor networks for sentiment analysis
Use gensim to obtain pretrained word vectors and compute similarities and analogies
Understand important foundations for openai chatgpt, gpt-4, dall-e, midjourney, and stable diffusion
Show more
Show less

Understand and implement word2vec
Understand the cbow method in word2vec
Understand the skip-gram method in word2vec
Understand the negative sampling optimization in word2vec
Understand and implement glove using gradient descent and alternating least squares
Use recurrent neural networks for parts-of-speech tagging
Use recurrent neural networks for named entity recognition
Understand and implement recursive neural networks for sentiment analysis
Understand and implement recursive neural tensor networks for sentiment analysis
Use gensim to obtain pretrained word vectors and compute similarities and analogies
Understand important foundations for openai chatgpt, gpt-4, dall-e, midjourney, and stable diffusion
Show more
Show less

Syllabus

Outline, Review, and Logistical Things

Introduction, Outline, and Review

How to Succeed in this Course

Where to get the code / data for this course

Preprocessed Wikipedia Data

How to Open Files for Windows Users

Beginner's Corner: Working with Word Vectors

What are vectors?

What is a word analogy?

Trying to find and assess word vectors using TF-IDF and t-SNE

Pretrained word vectors from GloVe

Pretrained word vectors from word2vec

Text Classification with word vectors

Text Classification in Code

Using pretrained vectors later in the course

Suggestion Box

Review of Language Modeling and Neural Networks

Review Section Intro

Bigrams and Language Models

Bigrams in Code

Neural Bigram Model

Neural Bigram Model in Code

Neural Network Bigram Model

Neural Network Bigram Model in Code

Improving Efficiency

Improving Efficiency in Code

Review Section Summary

Word Embeddings and Word2Vec

Return of the Bigram

CBOW

Skip-Gram

Hierarchical Softmax

Negative Sampling

Negative Sampling - Important Details

Why do I have 2 word embedding matrices and what do I do with them?

Word2Vec implementation tricks

Word2Vec implementation outline

Word2Vec in Code with Numpy

Tensorflow or Theano - Your Choice!

Word2Vec Tensorflow Implementation Details

Word2Vec Tensorflow in Code

Alternative to Wikipedia Data: Brown Corpus

Word Embeddings using GloVe

GloVe Section Introduction

Matrix Factorization for Recommender Systems - Basic Concepts

Matrix Factorization Training

Expanding the Matrix Factorization Model

Regularization for Matrix Factorization

GloVe - Global Vectors for Word Representation

Recap of ways to train GloVe

GloVe in Code - Numpy Gradient Descent

GloVe in Code - Alternating Least Squares

GloVe in Tensorflow with Gradient Descent

Visualizing country analogies with t-SNE

Hyperparameter Challenge

Training GloVe with SVD (Singular Value Decomposition)

Unifying Word2Vec and GloVe

Pointwise Mutual Information - Word2Vec as Matrix Factorization

PMI in Code

Using Neural Networks to Solve NLP Problems

Parts-of-Speech (POS) Tagging

How can neural networks be used to solve POS tagging?

Parts-of-Speech Tagging Baseline

Parts-of-Speech Tagging Recurrent Neural Network in Theano

Parts-of-Speech Tagging Recurrent Neural Network in Tensorflow

How does an HMM solve POS tagging?

Parts-of-Speech Tagging Hidden Markov Model (HMM)

Named Entity Recognition (NER)

Comparing NER and POS tagging

Named Entity Recognition Baseline

Named Entity Recognition RNN in Theano

Named Entity Recognition RNN in Tensorflow

Hyperparameter Challenge II

Recursive Neural Networks (Tree Neural Networks)

Recursive Neural Networks Section Introduction

Sentences as Trees

Data Description for Recursive Neural Networks

What are Recursive Neural Networks / Tree Neural Networks (TNNs)?

Building a TNN with Recursion

Trees to Sequences

Recursive Neural Tensor Networks

RNTN in Tensorflow (Tips)

RNTN in Tensorflow (Code)

Recursive Neural Network in TensorFlow with Recursion

Theano and Tensorflow Basics Review

(Review) Theano Basics

(Review) Theano Neural Network in Code

(Review) Tensorflow Basics

(Review) Tensorflow Neural Network in Code

Setting Up Your Environment (FAQ by Student Request)

Pre-Installation Check

Anaconda Environment Setup

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

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

How to install wp2txt or WikiExtractor.py

How to Uncompress a .tar.gz file

How to Code by Yourself (part 1)

How to Code by Yourself (part 2)

Proof that using Jupyter Notebook is the same as not using it

Python 2 vs Python 3

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops foundational knowledge of natural language processing and machine learning, which strengthens a base for intermediate learners

Covers building and understanding algorithms, which can give learners a stronger foundation in foundational computer science

Offers hands-on labs and interactive materials, which may contribute to a well-rounded learning experience

Taught by seasoned instructors who explicitly share they are accessible for support, which contributes to learner success

Advises learners about the order to take the instructor's courses in, which may prevent learners from being discouraged by taking on too much too soon

Assumes extensive coding experience in Python, which may exclude otherwise qualified learners who do not have extensive coding skills

Reviews summary

Top-rated nlp course

Learners say this top-rated NLP course is excellent with engaging assignments and a top-notch instructor giving students the knowledge and skills they need to advance in their careers.

Lots of great exercises

"A lot of great exercises"

Top-notch instructor

"top notch instructor"

Excellent course in advanced NLP.

"Best course in advanced NLP"

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 Natural Language Processing with Deep Learning in Python with these activities:

Compile a list of resources on NLP for future reference

Show steps

Organize and gather resources to support your NLP learning journey.

Show steps

Create a document or spreadsheet to store your resources.
Categorize resources based on topic, such as tutorials, research papers, and datasets.
Continuously add to your list as you discover new resources.

Read 'Deep Learning' by Goodfellow, Bengio, and Courville

Show steps

Gain a comprehensive understanding of deep learning concepts and techniques.

View Deep Learning on Amazon

Show steps

Read the book thoroughly, taking notes and highlighting important concepts.
Work through the exercises and examples provided in the book to reinforce your understanding.
Discuss the concepts with peers or mentors to deepen your comprehension.

Join a study group with peers to discuss NLP concepts

Show steps

Collaborate with peers to enhance your understanding of NLP.

Show steps

Find or create a study group with other students who are taking the same or similar NLP courses.
Meet regularly to discuss lecture material, work on assignments together, and ask questions.
Take turns presenting concepts to the group to reinforce your understanding.

Five other activities

Expand to see all activities and additional details

Show all eight activities

Solve NLP problems using recurrent neural networks

Show steps

Reinforce your understanding of recurrent neural networks by solving NLP problems.

Show steps

Identify NLP problems that can be solved using recurrent neural networks, such as parts-of-speech tagging or named entity recognition.
Implement recurrent neural network models in your preferred programming language.
Train and evaluate your models on real-world datasets.

Attend an NLP workshop on advanced techniques

Show steps

Gain exposure to cutting-edge NLP techniques and applications.

Show steps

Search for NLP workshops or conferences that focus on advanced techniques.
Identify workshops that align with your interests and learning goals.
Register for the workshop and actively participate in the sessions.

Develop a chatbot using NLP techniques

Show steps

Apply your NLP knowledge to build a practical application.

Show steps

Design the chatbot's functionality and user interface.
Implement natural language processing techniques for understanding user input.
Develop a knowledge base or retrieval system to provide relevant responses.
Train and evaluate the chatbot using real-world data.
Deploy the chatbot and gather feedback for further improvement.

Follow a TensorFlow tutorial on word embeddings

Show steps

Deepen your understanding of word embeddings by following a guided tutorial on TensorFlow.

Show steps

Find a comprehensive TensorFlow tutorial on word embeddings.
Follow the tutorial step-by-step, implementing the code yourself.
Experiment with different parameters and datasets to observe the impact on embedding quality.

Build a custom word embedding model for a specific domain

Show steps

Develop your skills in creating and customizing word embedding models.

Show steps

Identify a specific domain for which you want to create a custom word embedding model, such as healthcare or finance.
Collect a large corpus of text data from the chosen domain.
Preprocess the text data, including tokenization, stop word removal, and lemmatization.
Train a word embedding model using the preprocessed data, experimenting with different parameters and algorithms.
Evaluate the performance of your model on a downstream NLP task, such as text classification or question answering.

Career center

Learners who complete Natural Language Processing with Deep Learning in Python will develop knowledge and skills that may be useful to these careers:

Data Scientist

As a Data Scientist, you will use your understanding of word embeddings, recurrent neural networks, and recursive neural networks to build models that can process and analyze large amounts of text data. This course will provide you with the skills you need to succeed in this role, including how to use NLP techniques to solve real-world problems.

See salaries and explore the career path for Data Scientist

Natural Language Processing Engineer

As a Natural Language Processing Engineer, you will use your knowledge of word embeddings, recurrent neural networks, and recursive neural networks to develop and implement NLP solutions for a variety of applications. This course will provide you with the skills you need to succeed in this role, including how to use NLP techniques to build models that can understand and generate human language.

See salaries and explore the career path for Natural Language Processing Engineer

Machine Learning Engineer

As a Machine Learning Engineer, you will use your understanding of word embeddings, recurrent neural networks, and recursive neural networks to build and deploy machine learning models that can solve complex problems. This course will provide you with the skills you need to succeed in this role, including how to use NLP techniques to improve the performance of machine learning models.

See salaries and explore the career path for Machine Learning Engineer

Software Engineer

As a Software Engineer, you will use your knowledge of word embeddings, recurrent neural networks, and recursive neural networks to develop and implement software solutions for a variety of applications. This course will provide you with the skills you need to succeed in this role, including how to use NLP techniques to build software that can understand and interact with human language.

See salaries and explore the career path for Software Engineer

Data Analyst

As a Data Analyst, you will use your understanding of word embeddings, recurrent neural networks, and recursive neural networks to analyze data and extract insights. This course will provide you with the skills you need to succeed in this role, including how to use NLP techniques to identify trends and patterns in data.

See salaries and explore the career path for Data Analyst

Business Analyst

As a Business Analyst, you will use your understanding of word embeddings, recurrent neural networks, and recursive neural networks to analyze business data and make recommendations. This course will provide you with the skills you need to succeed in this role, including how to use NLP techniques to identify opportunities and risks.

See salaries and explore the career path for Business Analyst

Marketing Manager

As a Marketing Manager, you will use your understanding of word embeddings, recurrent neural networks, and recursive neural networks to develop and implement marketing campaigns. This course will provide you with the skills you need to succeed in this role, including how to use NLP techniques to target customers and create effective marketing messages.

See salaries and explore the career path for Marketing Manager

Sales Manager

As a Sales Manager, you will use your understanding of word embeddings, recurrent neural networks, and recursive neural networks to develop and implement sales strategies. This course will provide you with the skills you need to succeed in this role, including how to use NLP techniques to identify and close deals.

See salaries and explore the career path for Sales Manager

Product Manager

As a Product Manager, you will use your understanding of word embeddings, recurrent neural networks, and recursive neural networks to develop and launch new products. This course will provide you with the skills you need to succeed in this role, including how to use NLP techniques to understand customer needs and develop products that meet those needs.

See salaries and explore the career path for Product Manager

Technical Writer

As a Technical Writer, you will use your understanding of word embeddings, recurrent neural networks, and recursive neural networks to create technical documentation. This course will provide you with the skills you need to succeed in this role, including how to use NLP techniques to write clear and concise technical documentation.

See salaries and explore the career path for Technical Writer

Customer Success Manager

As a Customer Success Manager, you will use your understanding of word embeddings, recurrent neural networks, and recursive neural networks to build and maintain relationships with customers. This course will provide you with the skills you need to succeed in this role, including how to use NLP techniques to resolve customer issues and improve customer satisfaction.

See salaries and explore the career path for Customer Success Manager

UX Designer

As a UX Designer, you will use your understanding of word embeddings, recurrent neural networks, and recursive neural networks to design user interfaces. This course will provide you with the skills you need to succeed in this role, including how to use NLP techniques to create user interfaces that are easy to use and understand.

See salaries and explore the career path for UX Designer

Social Media Manager

As a Social Media Manager, you will use your understanding of word embeddings, recurrent neural networks, and recursive neural networks to manage social media accounts. This course will provide you with the skills you need to succeed in this role, including how to use NLP techniques to create social media content that is engaging and effective.

See salaries and explore the career path for Social Media Manager

Content Writer

As a Content Writer, you will use your understanding of word embeddings, recurrent neural networks, and recursive neural networks to create content for a variety of purposes. This course will provide you with the skills you need to succeed in this role, including how to use NLP techniques to create content that is engaging and informative.

See salaries and explore the career path for Content Writer

Community Manager

As a Community Manager, you will use your understanding of word embeddings, recurrent neural networks, and recursive neural networks to manage online communities. This course will provide you with the skills you need to succeed in this role, including how to use NLP techniques to build and maintain a strong online community.

See salaries and explore the career path for Community Manager