Machine Learning: Natural Language Processing in Python (V2)

Machine Learning: Natural Language Processing in Python (V2)
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch

What you’ll 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 text summarization
How to implement latent semantic indexing
How to implement topic modeling
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
Requirements
Install Python, it’s free!
Decent Python programming skills
Description
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

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.

Building a text classifier

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.

Spam detection

Sentiment analysis

Latent semantic analysis (also known as latent semantic indexing)

Topic modeling

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)

You’ll learn about:

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.

VIP-only: In the VIP version of this course, you will get your first taste of the power of Transformers. In this section, we will use the Hugging Face library to apply pre-trained NLP Transformer models to tasks such as:

Sentiment analysis

Text generation and language modeling

Question answering

Zero-shot classification

You’ll notice the first few tasks have been seen earlier in the course. This is intentional.

To end the section, we will go beyond just the familiar tasks to look at some very impressive feats of the modern NLP era, like zero-shot classification.

Thank you for reading and I hope to see you soon!

Who this course is for
Anyone who wants to learn natural language processing (NLP)
Anyone who wants to go beyond typical beginner-only courses on Udemy