Machine Learning: Natural Language Processing in Python (V2) 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.

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.

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
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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

Where To Get the Code

How to Succeed in This Course

Temporary 403 Errors

Vector Models and Text Preprocessing

Vector Models & Text Preprocessing Intro

Basic Definitions for NLP

What is a Vector?

Bag of Words

Count Vectorizer (Theory)

Tokenization

Stopwords

Stemming and Lemmatization

Stemming and Lemmatization Demo

Count Vectorizer (Code)

Vector Similarity

TF-IDF (Theory)

(Interactive) Recommender Exercise Prompt

TF-IDF (Code)

Word-to-Index Mapping

How to Build TF-IDF From Scratch

Neural Word Embeddings

Neural Word Embeddings Demo

Vector Models & Text Preprocessing Summary

Text Summarization Preview

How To Do NLP In Other Languages

Suggestion Box

Probabilistic Models (Introduction)

Markov Models (Intermediate)

Markov Models Section Introduction

The Markov Property

The Markov Model

Probability Smoothing and Log-Probabilities

Building a Text Classifier (Theory)

Building a Text Classifier (Exercise Prompt)

Building a Text Classifier (Code pt 1)

Building a Text Classifier (Code pt 2)

Language Model (Theory)

Language Model (Exercise Prompt)

Language Model (Code pt 1)

Language Model (Code pt 2)

Markov Models Section Summary

Article Spinner (Intermediate)

Article Spinning - Problem Description

Article Spinning - N-Gram Approach

Article Spinner Exercise Prompt

Article Spinner in Python (pt 1)

Article Spinner in Python (pt 2)

Case Study: Article Spinning Gone Wrong

Cipher Decryption (Advanced)

Section Introduction

Ciphers

Language Models (Review)

Genetic Algorithms

Code Preparation

Code pt 1

Code pt 2

Code pt 3

Code pt 4

Code pt 5

Code pt 6

Cipher Decryption - Additional Discussion

Section Conclusion

Machine Learning Models (Introduction)

Spam Detection

Spam Detection - Problem Description

Naive Bayes Intuition

Spam Detection - Exercise Prompt

Aside: Class Imbalance, ROC, AUC, and F1 Score (pt 1)

Aside: Class Imbalance, ROC, AUC, and F1 Score (pt 2)

Spam Detection in Python

Sentiment Analysis

Sentiment Analysis - Problem Description

Logistic Regression Intuition (pt 1)

Multiclass Logistic Regression (pt 2)

Logistic Regression Training and Interpretation (pt 3)

Sentiment Analysis - Exercise Prompt

Sentiment Analysis in Python (pt 1)

Sentiment Analysis in Python (pt 2)

Text Summarization

Text Summarization Section Introduction

Text Summarization Using Vectors

Text Summarization Exercise Prompt

Text Summarization in Python

TextRank Intuition

TextRank - How It Really Works (Advanced)

TextRank Exercise Prompt (Advanced)

TextRank in Python (Advanced)

Text Summarization in Python - The Easy Way (Beginner)

Text Summarization Section Summary

Topic Modeling

Topic Modeling Section Introduction

Latent Dirichlet Allocation (LDA) - Essentials

LDA - Code Preparation

LDA - Maybe Useful Picture (Optional)

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Explores the foundational concepts of natural language processing (NLP) by breaking it down into four parts

Covers advanced deep learning methods that are highly relevant to industry

Suitable for students with a wide range of experience levels, from beginner to advanced

Provides a strong foundation for those looking to build a career in NLP

Taught by instructors who are recognized for their work in 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 Machine Learning: Natural Language Processing in Python (V2) with these activities:

Review probability theory and Markov models

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Review these essential concepts to enhance your understanding of language models and machine learning methods in NLP

Browse courses on Probability Theory

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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

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Expand your knowledge of NLP concepts and techniques by reading this comprehensive textbook

View Natural Language Processing with Python:... on Amazon

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Connect with NLP experts

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Seek guidance and mentorship from experienced NLP professionals

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Four other activities

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Show all seven activities

Complete the NLP tutorial series on Coursera

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Enhance your practical skills by following a guided tutorial series on NLP

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Follow the video lectures and complete the assignments
Join the discussion forums and interact with other learners

Practice vector similarity calculations

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Reinforce your understanding of vector similarity and its applications in document retrieval, search engines, and text summarization

Browse courses on Text Preprocessing

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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

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Gain hands-on experience and insights by attending an NLP workshop

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Develop a text summarization algorithm

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Apply your knowledge of vector models, machine learning, and deep learning to build a practical text summarization tool

Browse courses on Text Summarization

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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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Learners who complete Machine Learning: Natural Language Processing in Python (V2) will develop knowledge and skills that may be useful to these careers:

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Machine Learning

Natural Language Processing in Python (V2)

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