Why SK-NLP ?

Skillknox has developed the world’s best NLP certification. Combining the intellect of academicians, experience of NLP professionals, Skillknox is looking to address the skill-gap in the industry through a comprehensive and carefully curated certification program in NLP.

Key Takeaways

  • Overview of Machine Learning & It’s applications
  • Supervised Vs Unsupervised Vs Reinforcement learning
  • Mathematical Understanding of Algorithms
  • Develop machine learning models and learn parameter tuning
  • Exposure to Scikit-learn library

Target Audience

This course is designed for the aspiring Data Analyst, Data Scientist, ML engineers, Deep Learning Engineers

Pre requisites

Introduction to Fundamentals of Python Programming, Data Analysis with Python

Module 01: Introduction

  • Introduction on NLP
  • Components & Applications
  • Challenges in NLP
  • Approaches to NLP
  • Computational Linguistics
  • Machine Learning
  • Deep Learning

Module 02: Basic Text Processing

  • NLP tasks in syntax
  • semantics, and pragmatics
  • Applications such as information extraction
  • question answering, and machine translation
  • The problem of ambiguity
  • The role of machine learning

Module 03: Minimum Edit Distance

  • Regular Expressions
  • Regular Expressions in Practical NLP
  • Word Tokenization
  • Word Normalization
  • Stemming-Sentence Segmentation

Module 04: Language Modeling

  • Defining Minimum Edit Distance
  • Computing Minimum Edit Distance
  • Backtrace for Computing Alignments
  • Weighted Minimum Edit Distance
  • Minimum Edit Distance in Computational Biology

Module 05: Spelling Correction

  • Introduction to N-grams
  • Estimating N-gram Probabilities
  • Evaluation and Perplexity
  • Generalization and Zeros & Smoothing
  • Add-One, Interpolation
  • Good-Turing Smoothing & Kneser-Ney Smoothing

Module 06: Text Classification

  • The Spelling Correction Task
  • The Noisy Channel Model of Spelling
  • Real-Word Spelling Correction
  • State of the Art Systems

Module 07: Sentiment Analysis

  • What is Text Classification
  • Naive Bayes– Formalizing the Naive Bayes Classifier
  • Naive Bayes – Learning
  • Naive Bayes – Relationship to Language Modeling
  • Multinomial Naive Bayes
  • A Worked Example – Precision, Recall, and the F measure
  • Text Classification, Evaluation
  • Practical Issues in Text Classification

Module 08: Maximum Entropy Classifiers

  • What is Sentiment Analysis
  • Sentiment Analysis
  • A baseline algorithm
  • Sentiment Lexicons
  • Learning Sentiment Lexicons
  • Other Sentiment Tasks

Module 09: Information Extraction and Named Entity Recognition

  • Generative vs Discriminative Models
  • Making features from text for discriminative
  • Feature-Based Linear Classifiers
  • Building a Maxent Model – The Nuts and Bolts
  • Generative vs. Discriminative models
  • The problem of overcounting evidence
  • Maximizing the Likelihood

Module 10: Relation Extraction

  • Introduction to Information Extraction
  • Evaluation of Named Entity Recognition
  • Sequence Models for Named Entity Recognition
  • Maximum Entropy Sequence Models.

Module 11: Advanced Maximum Entropy Models

  • What is Relation Extraction
  • Using Patterns to Extract Relations
  • Supervised Relation Extraction
  • Semi-Supervised and Unsupervised Relation Extraction

Module 12: POS Tagging

  • The Maximum Entropy Model Presentation
  • Feature Overlap_Feature Interaction
  • Conditional Maxent Models for Classification
  • Smoothing Regularization Priors for Maxent Models.

Module 13: Parsing Introduction

  • An Intro to Parts of Speech and POS Tagging
  • Some Methods and Results on Sequence Models for POS Tagging
  • Syntactic Structure – Constituency vs Dependency
  • Empirical Data-Driven Approach to Parsing

Module 14: Probabilistic Parsing

  • CFGs and PCFGs
  • Grammar Transforms
  • CKY Parsing
  • CKY Example
  • Constituency Parser Evaluation

Module 15: Lexicalized Parsing

  • Lexicalization of PCFGs Charniak’s Model
  • PCFG Independence Assumptions
  • The Return of Unlexicalized PCFGs
  • Latent Variable PCFGs

Module 16: Dependency Parsing

  • Dependency Parsing Introduction
  • Greedy Transition-Based Parsing
  • Dependencies Encode Relational Structure

Module 17: Information Retrieval

  • Introduction to Information Retrieval
  • Term-Document Incidence Matrices
  • The Inverted Index
  • Query Processing with the Inverted Index
  • Phrase Queries and Positional Indexes

Module 18: Ranked Information Retrieval

  • Introducing Ranked Retrieval
  • Scoring with the Jaccard Coefficient
  • Term Frequency Weighting
  • Inverse Document Frequency Weighting
  • TF-IDF Weighting
  • The Vector Space Model
  • Calculating TF-IDF Cosine Scores
  • Evaluating Search Engines

Module 19: Semantics

  • Word Senses and Word Relations
  • WordNet and Other Online Thesauri
  • Word Similarity and Thesaurus Methods
  • Word Similarity_ Distributional Similarity I
  • Word Similarity_ Distributional Similarity II

Module 20: Question Answering

  • What is Question Answering
  • Answer Types and Query Formulation
  • Passage Retrieval and Answer Extraction
  • Using Knowledge in QA
  • Advanced – Answering Complex Questions

Module 21: Summarization

  • Introduction to Summarization
  • Generating Snippets
  • Evaluating Summaries
  • ROUGE– Summarizing Multiple Documents.

40 hours of instructor led training



Module 01: Introduction

  • Introduction on NLP
  • Components & Applications
  • Challenges in NLP
  • Approaches to NLP
  • Computational Linguistics
  • Machine Learning
  • Deep Learning

Module 02: Basic Text Processing

  • NLP tasks in syntax
  • semantics, and pragmatics
  • Applications such as information extraction
  • question answering, and machine translation
  • The problem of ambiguity
  • The role of machine learning

Module 03: Minimum Edit Distance

  • Regular Expressions
  • Regular Expressions in Practical NLP
  • Word Tokenization
  • Word Normalization
  • Stemming-Sentence Segmentation

Module 04: Language Modeling

  • Defining Minimum Edit Distance
  • Computing Minimum Edit Distance
  • Backtrace for Computing Alignments
  • Weighted Minimum Edit Distance
  • Minimum Edit Distance in Computational Biology

Module 05: Spelling Correction

  • Introduction to N-grams
  • Estimating N-gram Probabilities
  • Evaluation and Perplexity
  • Generalization and Zeros & Smoothing
  • Add-One, Interpolation
  • Good-Turing Smoothing & Kneser-Ney Smoothing

Module 06: Text Classification

  • The Spelling Correction Task
  • The Noisy Channel Model of Spelling
  • Real-Word Spelling Correction
  • State of the Art Systems

Module 07: Sentiment Analysis

  • What is Text Classification
  • Naive Bayes– Formalizing the Naive Bayes Classifier
  • Naive Bayes – Learning
  • Naive Bayes – Relationship to Language Modeling
  • Multinomial Naive Bayes
  • A Worked Example – Precision, Recall, and the F measure
  • Text Classification, Evaluation
  • Practical Issues in Text Classification

Module 08: Maximum Entropy Classifiers

  • What is Sentiment Analysis
  • Sentiment Analysis
  • A baseline algorithm
  • Sentiment Lexicons
  • Learning Sentiment Lexicons
  • Other Sentiment Tasks

Module 09: Information Extraction and Named Entity Recognition

  • Generative vs Discriminative Models
  • Making features from text for discriminative
  • Feature-Based Linear Classifiers
  • Building a Maxent Model – The Nuts and Bolts
  • Generative vs. Discriminative models
  • The problem of overcounting evidence
  • Maximizing the Likelihood

Module 10: Relation Extraction

  • Introduction to Information Extraction
  • Evaluation of Named Entity Recognition
  • Sequence Models for Named Entity Recognition
  • Maximum Entropy Sequence Models.

Module 11: Advanced Maximum Entropy Models

  • What is Relation Extraction
  • Using Patterns to Extract Relations
  • Supervised Relation Extraction
  • Semi-Supervised and Unsupervised Relation Extraction

Module 12: POS Tagging

  • The Maximum Entropy Model Presentation
  • Feature Overlap_Feature Interaction
  • Conditional Maxent Models for Classification
  • Smoothing Regularization Priors for Maxent Models.

Module 13: Parsing Introduction

  • An Intro to Parts of Speech and POS Tagging
  • Some Methods and Results on Sequence Models for POS Tagging
  • Syntactic Structure – Constituency vs Dependency
  • Empirical Data-Driven Approach to Parsing

Module 14: Probabilistic Parsing

  • CFGs and PCFGs
  • Grammar Transforms
  • CKY Parsing
  • CKY Example
  • Constituency Parser Evaluation

Module 15: Lexicalized Parsing

  • Lexicalization of PCFGs Charniak’s Model
  • PCFG Independence Assumptions
  • The Return of Unlexicalized PCFGs
  • Latent Variable PCFGs

Module 16: Dependency Parsing

  • Dependency Parsing Introduction
  • Greedy Transition-Based Parsing
  • Dependencies Encode Relational Structure

Module 17: Information Retrieval

  • Introduction to Information Retrieval
  • Term-Document Incidence Matrices
  • The Inverted Index
  • Query Processing with the Inverted Index
  • Phrase Queries and Positional Indexes

Module 18: Ranked Information Retrieval

  • Introducing Ranked Retrieval
  • Scoring with the Jaccard Coefficient
  • Term Frequency Weighting
  • Inverse Document Frequency Weighting
  • TF-IDF Weighting
  • The Vector Space Model
  • Calculating TF-IDF Cosine Scores
  • Evaluating Search Engines

Module 19: Semantics

  • Word Senses and Word Relations
  • WordNet and Other Online Thesauri
  • Word Similarity and Thesaurus Methods
  • Word Similarity_ Distributional Similarity I
  • Word Similarity_ Distributional Similarity II

Module 20: Question Answering

  • What is Question Answering
  • Answer Types and Query Formulation
  • Passage Retrieval and Answer Extraction
  • Using Knowledge in QA
  • Advanced – Answering Complex Questions

Module 21: Summarization

  • Introduction to Summarization
  • Generating Snippets
  • Evaluating Summaries
  • ROUGE– Summarizing Multiple Documents.


40 hours of instructor led training


Contact Us

+91 93 848408 00

Request more information

Training Schedules

14th December 2019

Course Starts in

Days
Hours
Minutes
Seconds

Signup now

Course Curriculum

No curriculum found !

Course Reviews

N.A

ratings
  • 5 stars0
  • 4 stars0
  • 3 stars0
  • 2 stars0
  • 1 stars0

No Reviews found for this course.

Contact Us

+91 93 848408 00


Request more information

0 STUDENTS ENROLLED
    Accredited Partner

    Looking to train your team?

    Contact Us
    © 2018 Ken & Headway - All rights reserved.
    Quick Enquiry
    close slider

    Your contact information

    I authorize Ken and Headway to contact me on the phone number / email provided here by me .
    X