Duration
Introduction to Natural Language Processing (NLP)
3 days
Overview
The area of Natural Language Processing (NLP) is a subarea of Artificial Intelligence that aims to make computers capable of understanding human language, both written and spoken. Some examples of practical applications are: translators between languages, translation from text to speech or speech to text, chatbots, automatic question and answer systems (Q&A), automatic generation of descriptions for images, generation of subtitles in videos, classification of sentiments in sentences, among many others! Learning this area can be the key to bringing real solutions to present and future needs!
Equip yourself and your team with the knowledge needed to thrive in this AI-driven era.
Outline
Python Text Basics
- Introduction to Python Text Basics
- Working with Text Files with Python
- Working with PDFs
- Regular Expressions
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
Part of Speech Tagging and Named 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
- Part Of Speech Assessment
Text Classification
- Introduction to Text Classification
- Machine Learning Overview
- Classification Metrics
- Confusion Matrix
- Scikit-Learn Primer - How to Use SciKit-Learn
- Scikit-Learn Primer
- Text Feature Extraction Overview
- Text Feature Extraction - Code Along Implementations
- Text Classification Code Along Project
Semantics and Sentiment Analysis
- 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
- Sentiment Analysis Code Along Movie Review Project
Topic Modeling
- Introduction to Topic Modeling Section
- Overview of Topic Modeling
- Latent Dirichlet Allocation Overview
- Latent Dirichlet Allocation with Python
- Non-negative Matrix Factorization Overview
- Non-negative Matrix Factorization with Python
- Topic Modeling Project
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
- Creating Chat Bots with Python
Programme scope, delivery arrangements and suitability are confirmed during enquiry.