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Prerequisites:

  • Certified Deep Learning Specialist CDLS

 

Training Program Description:

  • In this Program, we will cover everything you need to learn to become a world-class practitioner of NLP with Python.
  • We will begin with the basics of Natural Language Processing, utilizing the Natural Language Toolkit library for Python, as well as the state-of-the-art Spacy library for ultra-fast tokenization, parsing, entity recognition, and lemmatization of text.
  • We’ll understand fundamental NLP concepts such as stemming, lemmatization, stop words, phrase matching, tokenization, and more!
  • Next, we will cover Part-of-Speech tagging, where your Python scripts will be able to automatically assign words in a text to their appropriate part of speech, such as nouns, verbs, and adjectives, an essential part of building intelligent language systems.
  • We will also learn about named entity recognition, allowing your code to automatically understand concepts like money, time, companies, products, and more simply by supplying the text information.
  • Through state-of-the-art visualization libraries, we will be able to view these relationships in real-time.
  • Then we will move on to understanding machine learning with Scikit-Learn to conduct text classification, such as automatically building machine learning systems that can determine positive versus negative movie reviews or spam versus legitimate email messages.
  • We will expand this knowledge to more complex unsupervised learning methods for natural language processing, such as topic modeling, where our machine learning models will detect topics and major concepts from raw text files.
  • This Program even covers advanced topics, such as sentiment analysis of the text with the NLTK library and creating semantic word vectors with the Word2Vec algorithm.

 

Projects

  • This program is comprised of many career-oriented projects. Each project you build will be an opportunity to demonstrate what you have learned in the lessons. Your completed projects will become part of a career portfolio that will demonstrate to potential employers that you have skills in Natural Language Processing.
  • One of our main goals at EAII is to help you create a job-ready portfolio of completed projects. Building a project is one of the best ways to test the skills you have acquired and to demonstrate your newfound abilities to future employers or colleagues. Throughout this program, you'll have the opportunity to prove your skills by building the following projects
  • Building a project is one of the best ways both to test the skills you have acquired and to demonstrate your newfound abilities to future employers. Throughout this program, you’ll have the opportunity to prove your skills by building the following projects:
  • Project 1:  Python Text
  • Project 2:  NLP Basics Assessment
  • Project 3:  Part of Speech tagging
  • Project 4:  Text Classification
  • Project 5:  Semantics and sentiment analysis
  • Project 6:  Topic Modeling
  • Project 7:  Text Generation
  • Project 8:  Creating Chat Bots
  • Capstone Project

 

Program Duration: 5 weeks

Program Language: English / Arabic

Location: EPSILON AI INSTITUTE | Head Office

 

Participants will be granted a completion certificate from Epsilon AI Institute, USA if they attend a minimum of 80 percent of the direct contact hours of the Program and after fulfilling program requirements (passing both Final Exam and Project to obtain the Certificate)

 

COURSE CONTENTS

1.Python Text Basics

  • Introduction to Python Text Basics
  • Working with Text Files with Python
  • Working with PDFs
  • Regular Expressions
  • Project #1 – Python Text Basics

 

2.Natural language Processing Basics

  • Introduction to Natural Language Processing
  • Spacy Setup and Overview
  • What is Natural Language Processing?
  • Spacy Basics
  • Tokenization
  • Stemming
  • Lemmatization
  • Stop Words
  • Phrase Matching and Vocabulary
  • Attention in NLP
  • Project #2 – NLP Basics Assessment

 

3.Part of Speech tagging and name entity recognition

  • Introduction to Section on POS and NER
  • Part of Speech Tagging
  • Visualizing Part of Speech
  • Named Entity Recognition
  • Visualizing Named Entity Recognition
  • Sentence Segmentation
  • Project #3 – Part of Speech

 

4.Text Classification

  • Introduction to Section on POS and NER
  • Introduction to Text Classification
  • Machine Learning Overview
  • Classification Metrics
  • Confusion Matrix
  • Scikit-Learn Primer – How to Use SciKit-Learn
  • Scikit-Learn Primer – Code Along
  • Text Feature Extraction Overview
  • Text Feature ExtFraction – Code Along Implementations
  • Project #4 – Text Classification

5.Semantics and sentiment analysis

  • Introduction to Python Text Basics
  • Introduction to Semantics and Sentiment Analysis
  • Overview of Semantics and Word Vectors
  • Semantics and Word Vectors with Spacy
  • Sentiment Analysis Overview
  • Sentiment Analysis with NLTK
  • Project #5 – Sentiment Analysis

 

6.Topic Modeling

  • Introduction to Topic Modeling Section
  • Overview of Topic Modeling
  • Latent Dirichlet Allocation with Python
  • Non-negative Matrix Factorization Overview
  • Non-negative Matrix Factorization with Python
  • Project #6 – Topic Modeling

 

7.Deep Learning for NLP

  • Introduction to Deep Learning for NLP
  • The Basic Perceptron Model
  • Introduction to Neural Networks
  • Keras Basics
  • Recurrent Neural Network Overview
  • LSTMs, GRU, and Text Generation
  • Text Generation with LSTMs with Keras and Python
  • Chat Bots Overview
  • Project #7 – Creating Chat Bot with deployment

 

8.CAPSTONE PROJECT

 

Download Certified Natural Language Processing (NLP) Specialist Brochure PDF

     

     

     

     

    Course Curriculum

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