Natural Language Processing Developer (NLPD)

What Our Learners Say
Nandito Tan
Nandito Tan
Its very informative and detailed
Veron Vernando
Veron Vernando
Pelatihan yang menarik dalam mempelajari design thingking profesional
raja doly sirait
raja doly sirait
its a fun class and fun teaching skill from the presenter
Nico Tivano
Nico Tivano
Pengajar nya sangat menyenangkan
hamzah alfitrah
hamzah alfitrah
Frankie
Frankie
Nithish Rao Sivakumar
Nithish Rao Sivakumar
ramachandran R
ramachandran R
Madhumithra Ramesh
Madhumithra Ramesh
Got an hands on experience in digital transformation especially on new trending and revolutionary technologies like AI,ML,Cyber security, IOT, Big data analytics, etc...
ajith v
ajith v
nice experience and learned a more detailed knowledge
18 udhay babu 4304
18 udhay babu 4304
Great place to learn
Siva Ram
Siva Ram
Aathisankar V
Aathisankar V
I learnt Digital transformation course which is very iseful
Google User
Google User
PRASATH RAJARAMAN
PRASATH RAJARAMAN
Nice session and very interactive staff
SARAVANA KUMAR
SARAVANA KUMAR
Thanga Tamil Amudhan
Thanga Tamil Amudhan
Prince R
Prince R
Ashoka Ganesan
Ashoka Ganesan
Gopinath Ariputheran
Gopinath Ariputheran
HARISH P
HARISH P
The course was good and helpful
Sivakumar B
Sivakumar B
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Upcoming Batches
Schedule Time (SGT / UTC +8) Sessions Date
Full-Time 9:30am to 5:30pm
(Daily)
5-Sessions 8 to 12 Nov | 10 to 14 Jan | 14 to 18 Mar
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Course Information
  • Duration: 5 Day / 40 Hours

  • Certification: Participants will receive a Certificate of Competency upon successfully completing the course and passing the examination

  • Who Should Attend: Data Analyst, Finance Analyst, HR Analyst, System Analyst, CIO, or Anyone who are interested in pursuing a career in the areas of Artificial Intelligence
Course Objective

Acquire advanced knowledge and technical competency Natural Language Processing (NLP) and Deep Learning.

Learn the key components in Natural Language Processing (NLP), semantic analysis, and featured engineering with Python Programming.

Pre-Requisite

It is preferred that participants successfully completed and pass Certified Machine Learning Expert (CMLE) or Python Programming Essentials (PPE)


Examination

Participants are required to attempt an examination upon completion of course. This exam tests a candidate’s knowledge and skills related to Natural Language Processing (NLP) and Python Programming based on the syllabus covered

Module 1 Introduction to Natural Language Processing (NLP)

Topics Covered
  • What is Natural Language Processing (NLP)
  • Applications of Natural Language Processing (NLP)
  • Using Natural Language Processing with Python
  • Understanding and setting up Python NLTK package
Module 2 Understanding Data For Natural Language Processing (NLP)

Topics Covered
  • Introduction to Corpus and Corpora
  • Categorical and Qualitative Data Attributes
  • Numerical and Quantitative Data Attributes
  • File Formats for Corpora
  • Dataset Preparation for NLP Application
  • Web scraping
Module 3 Introduction to Morphological Analysis

Topics Covered
  • Understanding Morphology
  • Morphemes and Stem
  • Using Morphological Analysis to Identify a Word
  • Classification of Morphemes
  • Lexical Analysis
  • Tokens and Part of Speech tags
  • Stemming vs Lemmatization
  • Performing Syntactic Analytics
Module 4 Performing Semantic Analysis

Topics Covered
  • Semantic Analysis
  • Understanding Lexical Semantics
  • Hyponymy and hyponyms
  • Application of Semantic Analysis
Module 5 Ambiguity in Natural Language Processing (NLP)

Topics Covered
  • What is Ambiguity
  • Lexical Ambiguity
  • Syntactic Ambiguity
  • Semantic Ambiguity
  • Pragmatic Ambiguity
  • Performing Pragmatic Analysis
Module 6 Pre-Processing in Natural Language Processing

Topics Covered
  • Handling Corpus-Raw Text
  • Handling of Corpus-Raw Sentences
  • Preprocessing Essentials
  • Customized Preprocessing
Module 7 Featured Engineering and NLP Algorithms

Topics Covered
  • What is feature engineering?
  • Parsers and Parsing in Natural Language Processing (NLP)
  • POS Tagging and POS Taggers
  • Name Entity Recognition
  • Introduction to n-grams
  • Bag of Words
  • Statistics for NLP
  • Probabilistic Theory for NLP
Module 8 Key Components of Featured Engineering

Topics Covered
  • What is TF-IDF
  • Using of textblob
  • Python Scikit-learn
  • Vectorization, Normalization
  • Probabilistic Models
  • Indexes
Module 9 Advanced Featured Engineering Techniques

Topics Covered
  • Understanding Recall Word Embedding
  • Distributional Semantics with word2vec
  • Unsupervised Distribution Semantic Mode
  • Blackbox to Whitebox
  • Core Components of word2vec Model
  • Logic of word2vec Model
  • Neural Network Algorithm



Natural Language Processing Developer (NLPD) involves rigorous usage of real-time case studies, hands-on exercises and group discussion

What Past Participants Say
The examples and cases shared in class are delivered in a clear, structured and easy to understand manner. It provide me with confidence to kickstart my own Machine Learning project.

Falhuddin Bin Nurdin

This course provide me with the detailed information required and essential tips how to explore more into the subject. The trainer is very helpful throughout the course. With his encouragement, I can understand the topics with ease even when I am not experience in programming.

Shalini Karthik

Why CASUGOL

Customization of Programs for specific industry, organisation, government agencies, statutory boards.


Flexible programmes designed to cater to the individual needs of participants, whether for professional upskilling, or for general interest.

Benefit from contribution from leading Industry Experts, Academics, and Researchers from across the world.


Opportunities for employers to develop their workforce and for individuals to enhance their career.

Dynamic learning environment that providing participants with professional networking opportunity.


Online support for participants after the training.

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