Data Science Specialization – NLP

(18 weeks of Learning)

After Completion of this Course, You’ll be Skilled to Apply For:

Job Description – NLP Engineer/ Text Mining

Primary Domain – E-Commerce/ Banking, financial services and insurance (BFSI)/ Telecom/ Pharma/ ITeS

Course Content

After the completion of 22 Modules of Level 1 & Level 2, you are just 7 weeks away from becoming a “Data Scientist”.

Module 23: Introduction to NLP

Module 24: Basic Techniques and Terminologies

  • Types Of Models Language Model, Seq2Seq….
  • Feature extraction from text
  • Counting Text
  • Frequency Distribution
  • Conditional Frequency Distribution

Module 25: Python: Stemming, Tokenization, Lemmatization

  • Counting Text
  • NLTK basic
  • Tokenization
  • Normalization
  • Lemmatization

Module 26: Text Classification Techniques

  • Text Feature Extraction using Count Vectorizer, TF-IDF
  • Classification techniques using features
  • Linear models for sentiment analysis
  • Hashing trick in spam filtering
  • Introduction to Vector Space Models
  • Word Vectors
  • Skip Gram and CBOW
  • Implementation using gensim

Module 31: Topic Modelling

  • Semantic Text Similarity
  • Topic Modeling
  • Generative Models and LDA
  • Information Extraction

Module 32: Python: Feature extraction and topic model representation

  • LDA implementation using gensim
  • Text Similarity
  • LSA implemntation and comparison with LDA

Module 33: Introduction to Deep Learning

  • What is Deep Learning?
  • Network Architectures
  • Gradient Descent
  • Backpropagation
  • Optimization Techniques
  • Hyper-Parameter Tuning

Module 35: Recurrent Neural Networks, GRU

  • Text NN Architectures
  • LSTM (Long Short Term Memory)
  • Vanishing Gradient
  • Gated Recurrent Unit

Module 36: Python: RNN implementation using Keras

  • Word Suggester
  • Question Answering

Module 37: Deep Learning Case Study 1

Module 38: Deep Learning Case Study 2