Duration
5 days
Day 1
9:00am - 10:30am
Topic 1 Overview of NLP and Deep Learning
Overview of Natural Language Processing (NLP)
Applications of NLP
10:30am - 10:45am Morning Break
10:45am - 12:30pm
Deep Learning Approach to NLP
NLP Frameworks
12:30pm - 1:30pm Lunch Break
1:30pm - 3:30pm
Topic 2 Language Modelling
Introduction to SpaCy
Tokenization and Stop Words
Preparing Data
3:30pm - 3:45pm Afternoon Break
3:45pm - 5:00pm
Pre-processing
Cleaning Data
Interpreting Tokens
Day 2
9:00am - 10:30am
Identifying and Removing Stop Words
Working with Stemming and Lemmatizing Tokens
Pre-trained Statistical Language Models
10:30am - 10:45am Morning Break
10:45am - 12:30pm
Part of Speech (POS) & Syntactic Dependency
Named Entities Recognition (NER) and Lemmatization
Rule Based Matching
12:30pm - 1:30pm Lunch Break
1:30pm - 3:30pm
Topic 3 Word Embedding
Overview of Word Embedding
Comparing Similarity
Word2Vec & Glove
3:30pm - 3:45pm Afternoon Break
3:45pm - 5:00pm
CBOW and Skip-gram Models
Word2Vec Training with Gensim
Pre-trained Word2Vec Model
Day 3
9:00am - 10:30am
Topic 4 NLP Pipelines and Text Classification
Language Processing Pipeline
Create Custom Component
Model Training and Update
10:30am - 10:45am Morning Break
10:45am - 12:30pm
Text Classification
Save and Load Model
Scikit Learn Classifiers
12:30pm - 1:30pm Lunch Break
1:30pm - 3:30pm
N-gram Models
Introduction to Sentiment Analysis
Examine Machine Learning Algorithms
3:30pm - 3:45pm Afternoon Break
3:45pm - 5:00pm
Build Sentiment Analysis Model Using Document Term Matrix
Classifying Text Using SVM
Day 4
9:00am - 10:30am
Topic 5: Text Clustering
Cosine Distance
Hierarchical Clustering & Dendrogram
10:30am - 10:45am Morning Break
10:45am - 12:30pm
Text Clustering Workflow
Similarity Clustering with Hashing
12:30pm - 1:30pm Lunch Break
1:30pm - 3:30pm
Topic 6: Topic Modelling
Latent Semantic Indexing (LSP)
3:30pm - 3:45pm Afternoon Break
3:45pm - 5:00pm
Latent Dirichlet Allocation (LDA)
Day 5
9:00am - 10:30am
Topic 7 Natural Language Processing with Sequence Models
Basic Neural Networks. ...
Basics of Recurrent Neural Network (RNN)
10:30am - 10:45am Morning Break
10:45am - 12:30pm
LSTM Recurrent Neural Networks.
12:30pm - 1:30pm Lunch Break
1:30pm - 3:30pm
Topic 8 Attention and Transformer
Introduction to Attention and Transformer
Transformer-Based Pipelines
3:30pm - 3:45pm Afternoon Break
3:45pm - 5:00pm
Applications of Hugging FaceTransformer
Question Answering
Text Summarization
Programme scope, delivery arrangements and suitability are confirmed during enquiry.