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Natural Language Processing - AI/Robotics培训

 
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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】:北京中山学院/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
最近开间(周末班/连续班/晚班):2018年3月18日
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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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