Building Pipelines for Natural Language Understanding with Spark

Table of contents

1. Welcome to the Course 00:01:37
2. Part 1: Building a natural language processing and entity extraction pipeline on Scala & Spark
   1. Notebook 1: Introduction 00:02:35
   2. Annotation Library 00:04:15
   3. Basic Annotators 00:08:59
   4. Vocabulary Analysis 00:09:30
   5. Exercise: Building a stopword annotator 00:05:06
3. Part 2: Machine Learning Applications for Statistical Natural Language Understanding at Scale
   1. Notebook 2: Introduction 00:02:14
   2. Model-based Annotators 00:04:18
   3. Creating a Binary Classifier 00:14:38
   4. Exercise: Predicting score or popularity 00:05:30
4. Part 3: Topic Modeling on Natural Language with Scala, Spark and MLLib
   1. Notebook 3: Introduction 00:02:12
   2. K-Means clustering 00:07:03
   3. LDA topic modeling 00:07:39
   4. Exercise: Using topics for score or popularity prediction 00:02:36
5. Part 4: Deep Learning Applications for Natural Language Understanding with Scala, Spark and MLLib
   1. Notebook 4: Introduction 00:02:07
   2. Word2Vec 00:05:05
   3. Expanding genre entity lists 00:04:49
   4. Exercise: Using Word2Vec based features for score or popularity prediction 00:02:44