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.
Hello friends.
Read more
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.
Hello friends.
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.
Hello friends.
Welcome to Machine Learning: Natural Language Processing in Python (Version 2).
This is a massive 4-in-1 course covering:
1) Vector models and text preprocessing methods
2) Probability models and Markov models
3) Machine learning methods
4) Deep learning and neural network methods
In part 1, which covers vector models and text preprocessing methods, you will learn about why vectors are so essential in data science and artificial intelligence. You will learn about various techniques for converting text into vectors, such as the CountVectorizer and TF-IDF, and you'll learn the basics of neural embedding methods like word2vec, and GloVe.
You'll then apply what you learned for various tasks, such as:
Text classification
Document retrieval / search engine
Text summarization
Along the way, you'll also learn important text preprocessing steps, such as tokenization, stemming, and lemmatization.
You'll be introduced briefly to classic NLP tasks such as parts-of-speech tagging.
In part 2, which covers probability models and Markov models, you'll learn about one of the most important models in all of data science and machine learning in the past 100 years. It has been applied in many areas in addition to NLP, such as finance, bioinformatics, and reinforcement learning.
In this course, you'll see how such probability models can be used in various ways, such as:
Building a text classifier
Article spinning
Text generation (generating poetry)
Importantly, these methods are an essential prerequisite for understanding how the latest Transformer (attention) models such as BERT and GPT-3 work. Specifically, we'll learn about 2 important tasks which correspond with the pre-training objectives for BERT and GPT.
In part 3, which covers machine learning methods, you'll learn about more of the classic NLP tasks, such as:
Spam detection
Sentiment analysis
Latent semantic analysis (also known as latent semantic indexing)
Topic modeling
This section will be application-focused rather than theory-focused, meaning that instead of spending most of our effort learning about the details of various ML algorithms, you'll be focusing on how they can be applied to the above tasks.
Of course, you'll still need to learn something about those algorithms in order to understand what's going on. The following algorithms will be used:
Naive Bayes
Logistic Regression
Principal Components Analysis (PCA) / Singular Value Decomposition (SVD)
Latent Dirichlet Allocation (LDA)
These are not just "any" machine learning / artificial intelligence algorithms but rather, ones that have been staples in NLP and are thus an essential part of any NLP course.
In part 4, which covers deep learning methods, you'll learn about modern neural network architectures that can be applied to solve NLP tasks. Thanks to their great power and flexibility, neural networks can be used to solve any of the aforementioned tasks in the course.
You'll learn about:
Feedforward Artificial Neural Networks (ANNs)
Embeddings
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
The study of RNNs will involve modern architectures such as the LSTM and GRU which have been widely used by Google, Amazon, Apple, Facebook, etc. for difficult tasks such as language translation, speech recognition, and text-to-speech.
Obviously, as the latest Transformers (such as BERT and GPT-3) are examples of deep neural networks, this part of the course is an essential prerequisite for understanding Transformers.
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
Thank you for reading and I hope to see you soon.
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What's inside
Learning objectives
- How to convert text into vectors using countvectorizer, tf-idf, word2vec, and glove
- How to implement a document retrieval system / search engine / similarity search / vector similarity
- Probability models, language models and markov models (prerequisite for transformers, bert, and gpt-3)
- How to implement a cipher decryption algorithm using genetic algorithms and language modeling
- How to implement spam detection
- How to implement sentiment analysis
- How to implement an article spinner
- How to implement text summarization
- How to implement latent semantic indexing
- How to implement topic modeling with lda, nmf, and svd
- Machine learning (naive bayes, logistic regression, pca, svd, latent dirichlet allocation)
- Deep learning (anns, cnns, rnns, lstm, gru) (more important prerequisites for bert and gpt-3)
- Hugging face transformers (vip only)
- How to use python, scikit-learn, tensorflow, +more for nlp
- Text preprocessing, tokenization, stopwords, lemmatization, and stemming
- Parts-of-speech (pos) tagging and named entity recognition (ner)
- Understand important foundations for openai chatgpt, gpt-4, dall-e, midjourney, and stable diffusion
- Show more
- Show less
- How to convert text into vectors using countvectorizer, tf-idf, word2vec, and glove
- How to implement a document retrieval system / search engine / similarity search / vector similarity
- Probability models, language models and markov models (prerequisite for transformers, bert, and gpt-3)
- How to implement a cipher decryption algorithm using genetic algorithms and language modeling
- How to implement spam detection
- How to implement sentiment analysis
- How to implement an article spinner
- How to implement text summarization
- How to implement latent semantic indexing
- How to implement topic modeling with lda, nmf, and svd
- Machine learning (naive bayes, logistic regression, pca, svd, latent dirichlet allocation)
- Deep learning (anns, cnns, rnns, lstm, gru) (more important prerequisites for bert and gpt-3)
- Hugging face transformers (vip only)
- How to use python, scikit-learn, tensorflow, +more for nlp
- Text preprocessing, tokenization, stopwords, lemmatization, and stemming
- Parts-of-speech (pos) tagging and named entity recognition (ner)
- Understand important foundations for openai chatgpt, gpt-4, dall-e, midjourney, and stable diffusion
- Show more
- Show less
Syllabus
Introduction
Introduction and Outline
Are You Beginner, Intermediate, or Advanced? All are OK!
Getting Set Up
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Syllabus
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Activities
Review probability theory and Markov models
Show steps
Review these essential concepts to enhance your understanding of language models and machine learning methods in NLP
Browse courses on
Probability Theory
Show steps
-
Read textbooks or online resources on probability theory
-
Practice solving probability problems
-
Review materials on Markov models
-
Complete practice exercises on Markov models
Read 'Natural Language Processing with Python' by Steven Bird, Ewan Klein, and Edward Loper
Show steps
Expand your knowledge of NLP concepts and techniques by reading this comprehensive textbook
View
Natural Language Processing with Python:...
on Amazon
Show steps
Connect with NLP experts
Show steps
Seek guidance and mentorship from experienced NLP professionals
Show steps
Four other activities
Expand to see all activities and additional details
Show all seven activities
Complete the NLP tutorial series on Coursera
Show steps
Enhance your practical skills by following a guided tutorial series on NLP
Show steps
-
Follow the video lectures and complete the assignments
-
Join the discussion forums and interact with other learners
Practice vector similarity calculations
Show steps
Reinforce your understanding of vector similarity and its applications in document retrieval, search engines, and text summarization
Browse courses on
Text Preprocessing
Show steps
-
Implement cosine similarity and other similarity metrics
-
Practice calculating vector similarity for different text representations
-
Apply vector similarity to a simple document retrieval system
Attend an NLP workshop
Show steps
Gain hands-on experience and insights by attending an NLP workshop
Show steps
Develop a text summarization algorithm
Show steps
Apply your knowledge of vector models, machine learning, and deep learning to build a practical text summarization tool
Browse courses on
Text Summarization
Show steps
-
Research and understand different text summarization techniques
-
Implement a text summarization algorithm using Python
-
Evaluate the performance of your algorithm on various datasets
-
Write a report summarizing your findings
Review probability theory and Markov models
Show steps
Review these essential concepts to enhance your understanding of language models and machine learning methods in NLP
Browse courses on
Probability Theory
Show steps
- Read textbooks or online resources on probability theory
- Practice solving probability problems
- Review materials on Markov models
- Complete practice exercises on Markov models
Read 'Natural Language Processing with Python' by Steven Bird, Ewan Klein, and Edward Loper
Show steps
Expand your knowledge of NLP concepts and techniques by reading this comprehensive textbook
View
Natural Language Processing with Python:...
on Amazon
Show steps
Connect with NLP experts
Show steps
Seek guidance and mentorship from experienced NLP professionals
Show steps
Complete the NLP tutorial series on Coursera
Show steps
Enhance your practical skills by following a guided tutorial series on NLP
Show steps
- Follow the video lectures and complete the assignments
- Join the discussion forums and interact with other learners
Practice vector similarity calculations
Show steps
Reinforce your understanding of vector similarity and its applications in document retrieval, search engines, and text summarization
Browse courses on
Text Preprocessing
Show steps
- Implement cosine similarity and other similarity metrics
- Practice calculating vector similarity for different text representations
- Apply vector similarity to a simple document retrieval system
Attend an NLP workshop
Show steps
Gain hands-on experience and insights by attending an NLP workshop
Show steps
Develop a text summarization algorithm
Show steps
Apply your knowledge of vector models, machine learning, and deep learning to build a practical text summarization tool
Browse courses on
Text Summarization
Show steps
- Research and understand different text summarization techniques
- Implement a text summarization algorithm using Python
- Evaluate the performance of your algorithm on various datasets
- Write a report summarizing your findings
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