曙海教育集团
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Natural Language Processing - AI/Robotics培训

 
   班级规模及环境--热线:4008699035 手机:15921673576( 微信同号)
       坚持小班授课,为保证培训效果,增加互动环节,每期人数限3到5人。
   上课时间和地点
上课地点:【上海】:同济大学(沪西)/新城金郡商务楼(11号线白银路站) 【深圳分部】:电影大厦(地铁一号线大剧院站)/深圳大学成教院 【北京分部】:北京中山/福鑫大楼 【南京分部】:金港大厦(和燕路) 【武汉分部】:佳源大厦(高新二路) 【成都分部】:领馆区1号(中和大道) 【沈阳分部】:沈阳理工大学/六宅臻品 【郑州分部】:郑州大学/锦华大厦 【石家庄分部】:河北科技大学/瑞景大厦 【广州分部】:广粮大厦 【西安分部】:协同大厦
近开课时间(周末班/连续班/晚班):2019年1月26日
   实验设备
     ☆资深工程师授课
        
        ☆注重质量 ☆边讲边练

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   质量保障

        1、培训过程中,如有部分内容理解不透或消化不好,可免费在以后培训班中重听;
        2、课程完成后,授课老师留给学员手机和Email,保障培训效果,免费提供半年的技术支持。
        3、培训合格学员可享受免费推荐就业机会。

课程大纲
 

Detailed training outline

Introduction to NLP
Understanding NLP
NLP Frameworks
Commercial applications of NLP
Scraping data from the web
Working with various APIs to retrieve text data
Working and storing text corpora saving content and relevant metadata
Advantages of using Python and NLTK crash course
Practical Understanding of a Corpus and Dataset
Why do we need a corpus?
Corpus Analysis
Types of data attributes
Different file formats for corpora
Preparing a dataset for NLP applications
Understanding the Structure of a Sentences
Components of NLP
Natural language understanding
Morphological analysis - stem, word, token, speech tags
Syntactic analysis
Semantic analysis
Handling ambigiuty
Text data preprocessing
Corpus- raw text
Sentence tokenization
Stemming for raw text
Lemmization of raw text
Stop word removal
Corpus-raw sentences
Word tokenization
Word lemmatization
Working with Term-Document/Document-Term matrices
Text tokenization into n-grams and sentences
Practical and customized preprocessing
Analyzing Text data
Basic feature of NLP
Parsers and parsing
POS tagging and taggers
Name entity recognition
N-grams
Bag of words
Statistical features of NLP
Concepts of Linear algebra for NLP
Probabilistic theory for NLP
TF-IDF
Vectorization
Encoders and Decoders
Normalization
Probabilistic Models
Advanced feature engineering and NLP
Basics of word2vec
Components of word2vec model
Logic of the word2vec model
Extension of the word2vec concept
Application of word2vec model
Case study: Application of bag of words: automatic text summarization using simplified and true Luhn's algorithms
Document Clustering, Classification and Topic Modeling
Document clustering and pattern mining (hierarchical clustering, k-means, clustering, etc.)
Comparing and classifying documents using TFIDF, Jaccard and cosine distance measures
Document classifcication using Naïve Bayes and Maximum Entropy
Identifying Important Text Elements
Reducing dimensionality: Principal Component Analysis, Singular Value Decomposition non-negative matrix factorization
Topic modeling and information retrieval using Latent Semantic Analysis
Entity Extraction, Sentiment Analysis and Advanced Topic Modeling
Positive vs. negative: degree of sentiment
Item Response Theory
Part of speech tagging and its application: finding people, places and organizations mentioned in text
Advanced topic modeling: Latent Dirichlet Allocation
Case studies
Mining unstructured user reviews
Sentiment classification and visualization of Product Review Data
Mining search logs for usage patterns
Text classification
Topic modelling

 
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