Tutors

DataTrained

PROGRAM OVERVIEW

What you'll learn

  1. Master the skills to get computers to understand, process, and manipulate human language
  2. Build models on real data, and get hands-on experience with sentiment analysis, machine translation, and more
  3. Deep Learning Modelling Techniques for human languages

CURRICULUM

  Module 1

  • Intoduction to NLP Part 1
  • Intoduction to NLP Part 2
  • Applications of NLP 1
  • Applications of NLP 2
  • Applications of NLP 3
  • Applications of NLP 4
  • Applications of NLP 5
  • Challenges Involved in NLP Part 1
  • Challenges Involved in NLP Part 2
  • Challenges Involved in NLP Part 3
  • Steps involved in Overcoming the Challenges
  • Lexical Processing- Stop words
  • Lexical Processing Tokenizers
  • Lexical Processing Stemming and Lammetization
  • Lexical Processing Handson Part 1
  • Lexical Processing Handson Part 2
  • Lexical Processing Handson Notebook
  • Lexical Processing Handson Part 3
  • Steaming Lexical Processing Handson Notebook
  • Syntactic Processing
  • Syntactic Processing Lexicon Based POS Tagger
  • Syntactic Processing Rule Based POS Tagger
  • Hands on POS Taggers Part 1
  • Hands on POS Taggers Part-2
  • Hands on POS Taggers Part 3
  • Hands on POS Taggers Part 4
  • Hands on POS Taggers Part 5
  • Regular Expressions Introduction
  • Regular Expressions Hands on part 1
  • Regular Expressions Hands on part 2
  • Regular Expressions Hands on part 3
  • Regular Expressions Hands on part 5
  • Regular Expressions Hands on part -6
  • Semantic Processing Part 1
  • Semantic Processing Part 2
  • Semantic Processing Part 3

  Module 2

  • Deep Learning Inro
  • Structure of Neural Network Part 1
  • Structure of Neural Network Part 2
  • Structure of Neural Network Part 3
  • Structure of Neural Network Part 4
  • Structure of Neural Network Part 5
  • Arranging perceptrons to build Atrificial Neural network
  • Classification Topology
  • Regression Topology
  • Mandatory parameters for specifying ANN
  • Inputs of Neural Network Part 1
  • Inputs of a Neural Network Part 2
  • Outputs of a Neural Network Part 1
  • Outputs of a Neural Network Part 2
  • Assumptions of Neural Netwrok
  • What is Training of a Neural Network
  • Standard Notations for Parameters in a Neural Network Part 1
  • Standard Notations for Parameters in a Neural Network Part 2
  • Activation Function and Selection

  Module 3

  • Occurrence Matrix
  • Merits and demertits of Occurrence matrix
  • Introduction on CoOccurrence matrix
  • Continued Session on CoOccurrence matrix
  • Word Vectors
  • Word Embedding
  • LSA Introduction
  • LSA Indepth
  • LSA Complete
  • LSA Merits and demerits
  • LSA Python Handson Exercise
  • SkipGram Introduction
  • Continued Skipgram
  • Mathematical Explanation
  • Website Demo on Word Embedding
  • Word2Vec Introduction
  • Word2Vec Handson Part 1
  • Word2Vec Handson Part 2
  • Word2Vec Handson Part 3
  • Word2Vec Handson Complete
  • Glove Handson
  • Building Text Classification Using Word Embedding Part 1
  • Building Text Classification Using Word Embedding Part 2
  • Building Text Classification Using Word Embedding Part 3
  • SPAM HAM Classifier using RNN vs Word Embeddings
  • Python NoteBook of the HandsOn
  • Python NoteBook of Text Classification Video Series
  • Training Data
  • Test Data

  Module 4

  • Introduction to Sentiment Analysis
  • Sentiment Analysis Hands On Part 1
  • Sentiment Analysis Hands On Part 2
  • Sentiment Analysis Hands On Part 3
  • Sentiment Analysis Hands On Part 4
  • Sentiment Analysis Hands On Part 5
  • Sentiment Analysis Hands On Part-6
  • Sentiment Analysis Hands On Part 7
  • Sentiment Analysis Hands On Part 8
  • Sentiment Analysis Hands On Part 9
  • Sentiment Analysis Hands on Notebook
  • Kaggle Dataset Download Link