Certified Natural

Language Processing

Developer
 

Learn step-by-step what it takes to become a
Natural Language Processing Developer
 

6 Days / 48 Hours / 9 Modules
+ 3 month coaching + 1 year eLearning access
Certified Natural
Language Processing
Developer

Learn step-by-step what
it takes to become a Natural
Language Processing Developer


6 Days / 48 Hours / 10 Modules
+ 3 month coaching + 1 year eLearning access

Learn step-by-step what it takes to become a Natural Language Processing Developer

The Certified Natural Language Processing qualification gives you knowledge about NLP techniques and best practices with hands-on experience in delivering computer vision projects.
 
Key Learning Outcomes

This course will also give students a chance to understand the fundamental issues and challenges of machine learning which include data, model selection, and model complexity. This course will equip you with the necessary skills needed to excel in this field. By the end of the training program, you will be able to:

  • Understand Natural Language Processing (NLP) and its applications
  • Utilize the data obtained for NLP
  • Perform semantic analysis
  • Be able to handle Corpus-raw text and sentences
  • Understand engineering techniques involved in NLP
 
 
Key Learning Outcomes

This course will also give students a chance to understand the fundamental issues and challenges of machine learning which include data, model selection, and model complexity. This course will equip you with the necessary skills needed to excel in this field. By the end of the training program, you will be able to:

  • Become familiar with analyzing data, computing statistical measures along with Data Wrangling, Data Cleansing, Data Manipulation, etc.
  • Become familiar with Machine Learning algorithms including Black Box techniques such as Neural Networks and Support Vector Machine.
  • Become familiar with Regression algorithms and the application of Python as statistical software in Machine Learning and Data Science.
  • Build predictive models.
  • Be able to create Data Visualization, Data Manipulation in different forms and draw meaningful business insights from the underlying data.
 
Program Curriculum
Module 1 - Introduction to Natural Language Processing (NLP)

Introduction to Natural Language Processing (NLP)

  1. What is Natural Language Processing (NLP)
  2. Applications of Natural Language Processing (NLP)
  3. Using Natural Language Processing with Python
  4. Understanding and setting up Python NLTK package
Module 2 - Understanding Data For Natural Language Processing (NLP)

Understanding Data For Natural Language Processing (NLP)

  1. Introduction to Corpus and Corpora
  2. Categorical and Qualitative Data Attributes
  3. Numerical and Quantitative Data Attributes
  4. File Formats for Corpora
  5. Dataset Preparation for NLP Application
  6. Web scraping
Module 3 - Introduction to Morphological Analysis

Introduction to Morphological Analysis

  1. Understanding Morphology
  2. Morphemes and Stem
  3. Using Morphological Analysis to Identify a Word
  4. Classification of Morphemes
  5. Lexical Analysis
  6. Tokens and Part of Speech tags
  7. Stemming vs Lemmatization
  8. Performing Syntactic Analytics
Module 4 - Performing Semantic Analysis

Performing Semantic Analysis

  1. Semantic Analysis
  2. Understanding Lexical Semantics
  3. Hyponymy and hyponyms
  4. Application of Semantic Analysis
Module 5 - Ambiguity in Natural Language Processing (NLP)

Ambiguity in Natural Language Processing (NLP)

  1. What is Ambiguity
  2. Lexical Ambiguity
  3. Syntactic Ambiguity
  4. Semantic Ambiguity
  5. Pragmatic Ambiguity
  6. Performing Pragmatic Analysis
Module 6 - Pre-Processing in Natural Language Processing

Pre-Processing in Natural Language Processing

  1. Handling Corpus-Raw Text
  2. Handling of Corpus-Raw Sentences
  3. Preprocessing Essentials
  4. Customized Preprocessing
Module 7 - Featured Engineering and NLP Algorithms

Featured Engineering and NLP Algorithms

  1. What is feature engineering?
  2. Parsers and Parsing in Natural Language Processing (NLP)
  3. POS Tagging and POS Taggers
  4. Name Entity Recognition
  5. Introduction to n-grams
  6. Bag of Words
  7. Statistics for NLP
  8. Probabilistic Theory for NLP
Module 8 - Key Components of Featured Engineering

Key Components of Featured Engineering

  1. What is TF-IDF
  2. Using of textblob
  3. Python Scikit-learn
  4. Vectorization, Normalization
  5. Probabilistic Models
  6. Indexes
Module 9 - Advanced Featured Engineering Techniques

Key Components of Featured Engineering

  1. Understanding Recall Word Embedding
  2. Distributional Semantics with word2vec
  3. Unsupervised Distribution Semantic Mode
  4. Blackbox to Whitebox
  5. Core Components of word2vec Model
  6. Logic of word2vec Model
  7. Neural Network Algorithm
Program Key Highlights

online-learning-2
40 hours of Remote Online Learning
learning-hours
80 Additional Self Learning Hours
hands-on
12 Live Hands-on Projects
certification
Certified by International Body
mentor
Mentorship with Industry Experts
industry
Designed for Beginners & Professionals

Get Professionally Certified

Upon successfully completing this program, participants will be awarded the Professional Certification in Python Data Science by International Council for Technology Certifications (ICTC).
This award is a validation to the efforts taken to master the domain expertise that will set you apart from your competition.
Be a part of the global network of data science professionals and join the community across sectors.
Get in Touch With Us Today!

This training program is suitable for anyone who intends to enter into the field of Data Science and Machine Learning. This program is being conducted in Malaysia and can be joined by anyone, anywhere in the world remotely.
Program Fee


RM 3,700.00

One-time fee. One year access to course materials.
 
 



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