Natural Language Processing: NLP With Transformers in Python

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Course Overview

Lecture 3: Hello! and Further Resources

Lecture 4: Environment Setup

Lecture 5: Alternative Local Setup

Lecture 6: Alternative Colab Setup

Lecture 7: CUDA Setup

Lecture 8: Apple Silicon Setup

Chapter 2: NLP and Transformers

Lecture 1: The Three Eras of AI

Lecture 2: Pros and Cons of Neural AI

Lecture 3: Word Vectors

Lecture 4: Recurrent Neural Networks

Lecture 5: Long Short-Term Memory

Lecture 6: Encoder-Decoder Attention

Lecture 7: Self-Attention

Lecture 8: Multi-head Attention

Lecture 9: Positional Encoding

Lecture 10: Transformer Heads

Chapter 3: Preprocessing for NLP

Lecture 1: Stopwords

Lecture 2: Tokens Introduction

Lecture 3: Model-Specific Special Tokens

Lecture 4: Stemming

Lecture 5: Lemmatization

Lecture 6: Unicode Normalization – Canonical and Compatibility Equivalence

Lecture 7: Unicode Normalization – Composition and Decomposition

Lecture 8: Unicode Normalization – NFD and NFC

Lecture 9: Unicode Normalization – NFKD and NFKC

Chapter 4: Attention

Lecture 1: Attention Introduction

Lecture 2: Alignment With Dot-Product

Lecture 3: Dot-Product Attention

Lecture 4: Self Attention

Lecture 5: Bidirectional Attention

Lecture 6: Multi-head and Scaled Dot-Product Attention

Chapter 5: Language Classification

Lecture 1: Introduction to Sentiment Analysis

Lecture 2: Prebuilt Flair Models

Lecture 3: Introduction to Sentiment Models With Transformers

Lecture 4: Tokenization And Special Tokens For BERT

Lecture 5: Making Predictions

Chapter 6: [Project] Sentiment Model With TensorFlow and Transformers

Lecture 1: Project Overview

Lecture 2: Getting the Data (Kaggle API)

Lecture 3: Preprocessing

Lecture 4: Building a Dataset

Lecture 5: Dataset Shuffle, Batch, Split, and Save

Lecture 6: Build and Save

Lecture 7: Loading and Prediction

Chapter 7: Long Text Classification With BERT

Lecture 1: Classification of Long Text Using Windows

Lecture 2: Window Method in PyTorch

Chapter 8: Named Entity Recognition (NER)

Lecture 1: Introduction to spaCy

Lecture 2: Extracting Entities

Lecture 3: Authenticating With The Reddit API

Lecture 4: Pulling Data With The Reddit API

Lecture 5: Extracting ORGs From Reddit Data

Lecture 6: Getting Entity Frequency

Lecture 7: Entity Blacklist

Lecture 8: NER With Sentiment

Lecture 9: NER With roBERTa

Chapter 9: Question and Answering

Lecture 1: Open Domain and Reading Comprehension

Lecture 2: Retrievers, Readers, and Generators

Lecture 3: Intro to SQuAD 2.0

Lecture 4: Processing SQuAD Training Data

Lecture 5: (Optional) Processing SQuAD Training Data with Match-Case

Lecture 6: Our First Q&A Model

Chapter 10: Metrics For Language

Lecture 1: Q&A Performance With Exact Match (EM)

Lecture 2: Introducing the ROUGE Metric

Lecture 3: ROUGE in Python

Lecture 4: Applying ROUGE to Q&A

Lecture 5: Recall, Precision and F1

Lecture 6: Longest Common Subsequence (LCS)

Chapter 11: Reader-Retriever QA With Haystack

Lecture 1: Intro to Retriever-Reader and Haystack

Lecture 2: What is Elasticsearch?

Lecture 3: Elasticsearch Setup (Windows)

Lecture 4: Elasticsearch Setup (Linux)

Lecture 5: Elasticsearch in Haystack

Lecture 6: Sparse Retrievers

Lecture 7: Cleaning the Index

Lecture 8: Implementing a BM25 Retriever

Lecture 9: What is FAISS?

Lecture 10: Further Materials for Faiss

Lecture 11: FAISS in Haystack

Lecture 12: What is DPR?

Lecture 13: The DPR Architecture

Lecture 14: Retriever-Reader Stack

Chapter 12: [Project] Open-Domain QA

Lecture 1: ODQA Stack Structure

Lecture 2: Creating the Database

Lecture 3: Building the Haystack Pipeline

Chapter 13: Similarity