Machine Learning- Natural Language Processing in Python (V2)

Machine Learning: Natural Language Processing in Python (V2), available at $89.99, has an average rating of 4.8, with 177 lectures, based on 5352 reviews, and has 20494 subscribers.

You will learn about 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 This course is ideal for individuals who are Anyone who wants to learn natural language processing (NLP) or Anyone interested in artificial intelligence, machine learning, deep learning, or data science or Anyone who wants to go beyond typical beginner-only courses on Udemy It is particularly useful for Anyone who wants to learn natural language processing (NLP) or Anyone interested in artificial intelligence, machine learning, deep learning, or data science or Anyone who wants to go beyond typical beginner-only courses on Udemy.

Enroll now: Machine Learning: Natural Language Processing in Python (V2)

Summary

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

Price: $89.99

Average Rating: 4.8

Number of Lectures: 177

Number of Published Lectures: 160

Number of Curriculum Items: 177

Number of Published Curriculum Objects: 160

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

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

Who Should Attend

Anyone who wants to learn natural language processing (NLP)

Anyone interested in artificial intelligence, machine learning, deep learning, or data science

Anyone who wants to go beyond typical beginner-only courses on Udemy

Target Audiences

Anyone who wants to learn natural language processing (NLP)

Anyone interested in artificial intelligence, machine learning, deep learning, or data science

Anyone who wants to go beyond typical beginner-only courses on 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 BERTand 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 BERTand 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!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction and Outline

Lecture 2: Are You Beginner, Intermediate, or Advanced? All are OK!

Chapter 2: Getting Set Up

Lecture 1: Where To Get the Code

Lecture 2: How to Succeed in This Course

Lecture 3: Temporary 403 Errors

Chapter 3: Vector Models and Text Preprocessing

Lecture 1: Vector Models & Text Preprocessing Intro

Lecture 2: Basic Definitions for NLP

Lecture 3: What is a Vector?

Lecture 4: Bag of Words

Lecture 5: Count Vectorizer (Theory)

Lecture 6: Tokenization

Lecture 7: Stopwords

Lecture 8: Stemming and Lemmatization

Lecture 9: Stemming and Lemmatization Demo

Lecture 10: Count Vectorizer (Code)

Lecture 11: Vector Similarity

Lecture 12: TF-IDF (Theory)

Lecture 13: (Interactive) Recommender Exercise Prompt

Lecture 14: TF-IDF (Code)

Lecture 15: Word-to-Index Mapping

Lecture 16: How to Build TF-IDF From Scratch

Lecture 17: Neural Word Embeddings

Lecture 18: Neural Word Embeddings Demo

Lecture 19: Vector Models & Text Preprocessing Summary

Lecture 20: Text Summarization Preview

Lecture 21: How To Do NLP In Other Languages

Lecture 22: Suggestion Box

Chapter 4: Probabilistic Models (Introduction)

Lecture 1: Probabilistic Models (Introduction)

Chapter 5: Markov Models (Intermediate)

Lecture 1: Markov Models Section Introduction

Lecture 2: The Markov Property

Lecture 3: The Markov Model

Lecture 4: Probability Smoothing and Log-Probabilities

Lecture 5: Building a Text Classifier (Theory)

Lecture 6: Building a Text Classifier (Exercise Prompt)

Lecture 7: Building a Text Classifier (Code pt 1)

Lecture 8: Building a Text Classifier (Code pt 2)

Lecture 9: Language Model (Theory)

Lecture 10: Language Model (Exercise Prompt)

Lecture 11: Language Model (Code pt 1)

Lecture 12: Language Model (Code pt 2)

Lecture 13: Markov Models Section Summary

Chapter 6: Article Spinner (Intermediate)

Lecture 1: Article Spinning – Problem Description

Lecture 2: Article Spinning – N-Gram Approach

Lecture 3: Article Spinner Exercise Prompt

Lecture 4: Article Spinner in Python (pt 1)

Lecture 5: Article Spinner in Python (pt 2)

Lecture 6: Case Study: Article Spinning Gone Wrong

Chapter 7: Cipher Decryption (Advanced)

Lecture 1: Section Introduction

Lecture 2: Ciphers

Lecture 3: Language Models (Review)

Lecture 4: Genetic Algorithms

Lecture 5: Code Preparation

Lecture 6: Code pt 1

Lecture 7: Code pt 2

Lecture 8: Code pt 3

Lecture 9: Code pt 4

Lecture 10: Code pt 5

Lecture 11: Code pt 6

Lecture 12: Cipher Decryption – Additional Discussion

Lecture 13: Real-World Application: Acoustic Keylogger

Lecture 14: Section Conclusion

Chapter 8: Machine Learning Models (Introduction)

Lecture 1: Machine Learning Models (Introduction)

Chapter 9: Spam Detection

Lecture 1: Spam Detection – Problem Description

Lecture 2: Naive Bayes Intuition

Lecture 3: Spam Detection – Exercise Prompt

Lecture 4: Aside: Class Imbalance, ROC, AUC, and F1 Score (pt 1)

Lecture 5: Aside: Class Imbalance, ROC, AUC, and F1 Score (pt 2)

Lecture 6: Spam Detection in Python

Chapter 10: Sentiment Analysis

Lecture 1: Sentiment Analysis – Problem Description

Lecture 2: Logistic Regression Intuition (pt 1)

Lecture 3: Multiclass Logistic Regression (pt 2)

Lecture 4: Logistic Regression Training and Interpretation (pt 3)

Lecture 5: Sentiment Analysis – Exercise Prompt

Lecture 6: Sentiment Analysis in Python (pt 1)

Lecture 7: Sentiment Analysis in Python (pt 2)

Chapter 11: Text Summarization

Lecture 1: Text Summarization Section Introduction

Lecture 2: Text Summarization Using Vectors

Lecture 3: Text Summarization Exercise Prompt

Lecture 4: Text Summarization in Python

Lecture 5: TextRank Intuition

Lecture 6: TextRank – How It Really Works (Advanced)

Lecture 7: TextRank Exercise Prompt (Advanced)

Lecture 8: TextRank in Python (Advanced)

Lecture 9: Text Summarization in Python – The Easy Way (Beginner)

Lecture 10: Text Summarization Section Summary

Chapter 12: Topic Modeling

Lecture 1: Topic Modeling Section Introduction

Lecture 2: Latent Dirichlet Allocation (LDA) – Essentials

Lecture 3: LDA – Code Preparation

Instructors

Lazy Programmer Inc.
Artificial intelligence and machine learning engineer

Lazy Programmer Team
Artificial Intelligence and Machine Learning Engineer

Rating Distribution

1 stars: 24 votes

2 stars: 29 votes

3 stars: 94 votes

4 stars: 1429 votes

5 stars: 3776 votes

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