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آموزش شروع کار با پردازش زبان طبیعی با Python ،Spark و Scala

دسته بندی ها: آموزش اسکالا (Scala) ، آموزش پایتون (Python) ، آموزش های OReilly
Learning Path: Get Started with Natural Language Processing Using Python, Spark, and Scala Publisher:Oreilly Author:O'Reilly Media, Inc. Duration:05:48:44

Whether you’re a programmer with little to no knowledge of Python, or an experienced data scientist or engineer, this Learning Path will walk you through natural language processing, using ... - Selection from Learning Path: Get Started with Natural Language Processing Using Python, Spark, and Scala [Video]
Release Date: March 2017
ISBN: 9781491985854
Video Description
Whether you’re a programmer with little to no knowledge of Python, or an experienced data scientist or engineer, this Learning Path will walk you through natural language processing, using both Python and Scala, and show you how to implement a range of popular tools including Spark, scikit-learn, SpaCy, NLTK, and gensim for text mining.You’ll learn the most common techniques for processing text, how to use machine learning to generate annotators and apply them within a data pipeline, and the differences between NLP pipelines and other approaches to semantic text mining. You’ll learn about standard UIMA annotators, custom annotators, and machine-learned annotators, and understand how architectures for text processing pipelines can incorporate some of the most popular big data tools such as Kafka, Spark, SparkSQL, Cassandra, and ElasticSearch.By the end of the learning path, you will be able to build a natural language processing and entity extraction pipeline, and will have a complete understanding of the capabilities and limitations of natural language text processing.Materials or downloads needed in advance: Example files
Introduction
Course Introduction
00:02:25
About The Author
00:00:36
How To Access Your Working Files
00:01:15
Getting Started: Basic String Processing In Python
String Operations
00:04:49
Working With Unicode
00:05:16
Converting Text To Symbols: Tokenization In NLTK and spaCy
Splitting Documents
00:04:41
Splitting Sentences
00:03:20
Filtering Stop Words
00:02:07
Going Subsymbolic: Vector Representations
tf-idf Gensim
00:09:24
Word Vectors
00:03:35
Google Word Vectors
00:04:03
Learn Word Vectors
00:08:07
Finding The Structure Of Text: Parsing In spaCy
Dependency Parsing
00:03:39
Sentence Head
00:02:23
Named Entities
00:03:21
Determining How The Writer Feels: Sentiment Analysis In VADER
Sentiment Analysis Intro
00:03:18
Sentiment In VADER
00:05:13
Making Decisions: Text Classification
Text Classification Intro
00:02:45
Classification With TextBlob
00:10:25
Classification With scikit-learn
00:07:17
Indentifying Discussed Topics: LDA In Gensim
LDA Introduction
00:02:43
LDA Gensim
00:07:13
LDA pyLDAvis
00:03:54
Toward Machine Reading: Entity Extraction And Linking
Entity Linking
00:03:28
pyspotlight
00:03:16
FRED
00:03:16
Conclusion
Conclusion
00:02:24
Part 1: Introduction
Welcome to the Course
00:01:39
Natural Language Understanding in Examples
00:10:09
Part 2: NLP Pipelines
Building an NLP Pipeline
00:15:49
Part 3 - Annotators
Commonly Used Annotators
00:08:47
Detecting Positive, Negative & Speculative Polarity
00:12:09
Machine Learned Annotators
00:12:16
Part 4: Custom Annotators
NLP Pipelines are Domain Specific
00:06:55
Unified Medical Language System (UMLS)
00:03:33
Coding Custom Annotators
00:07:17
Part 5: Machine Learned Annotators
Training & Using Machine Learned Annotators
00:09:45
Part 6: Ontology Enrichment
The Need for Learned and Updated Ontologies
00:09:39
Learning New Medical Concepts and Relationships
00:19:37
Part 7: Architecture
An End-to-End Reference Architecture
00:04:19
Spark, SparkSQL, Cassandra Workflow
00:03:16
ElasticSearch & SparkSQL
00:06:52
Part 8: Parting Advice
Language is Source and Domain-Specific
00:09:32
Welcome to the Course
00:01:37
Part 1: Building a natural language processing and entity extraction pipeline on Scala & Spark
Notebook 1: Introduction
00:02:35
Annotation Library
00:04:15
Basic Annotators
00:08:59
Vocabulary Analysis
00:09:30
Exercise: Building a stopword annotator
00:05:06
Part 2: Machine Learning Applications for Statistical Natural Language Understanding at Scale
Notebook 2: Introduction
00:02:14
Model-based Annotators
00:04:18
Creating a Binary Classifier
00:14:38
Exercise: Predicting score or popularity
00:05:30
Part 3: Topic Modeling on Natural Language with Scala, Spark and MLLib
Notebook 3: Introduction
00:02:12
K-Means clustering
00:07:03
LDA topic modeling
00:07:39
Exercise: Using topics for score or popularity prediction
00:02:36
Part 4: Deep Learning Applications for Natural Language Understanding with Scala, Spark and MLLib
Notebook 4: Introduction
00:02:07
Word2Vec
00:05:05
Expanding genre entity lists
00:04:49
Exercise: Using Word2Vec based features for score or popularity prediction
00:02:44

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