Solving Business Problems with NLP

Solving business problems with NLP
Start Date: February 6, 2022
Last date to register:
Course duration: 40 hours
Cost: donation
Skill level: beginner

Course Description

For whom is this course

This course is for anyone interested in learning & applying Natural Language Processing (NLP) for solving real-world business problems

Objective

  • Cleaning & vectorization of text data (CountVectorizer, Tf-IDF, Word-2-Vec)
  • Working with a mix of numeric and text data
  • Visualization of Text data e.g. wordcloud
  • Advanced application of NLP e.g. Fuzzy Name Matching
  • Named Entity Recognition and topic classification using NLP
  • Supervised, semi-supervised, and unsupervised classification in NLP
  • Transformer models and their application in NLP

What you will learn

  • Technical skills are essential, but not enough, non-technical and domain fields of studies are still essential if you want to understand data science vs its application.
  • Current and future global challenges in the sector
  • How data science or artificial intelligence would be applied.
  • Data science and the necessities to keep learning for life.
  • Instructor-led online course with guided labs
  • Real-world, practical assignment(s) leading to project
  • Application in social and business problems

Prerequisites

  • Basic Python

Syllabus

Week Instruction
(1 hr)
Lab (guided + unguided)
1+ 3 hrs
Week 1
(5 hrs)
Developing Text dataset
Web and pdf scraping
Nocode and with code Web Scraping for developing a text dataset
Tools: ParseHub, Beautiful Soup
Week 2
(5 hrs)
Pre processing, cleaning & visualization of text data Stop word & punctuation removal, stemming & lemmatization, Word-to-vector, Vectorization (Tf-Idf, Count vectorizer)
Tools: NLTK, Spacy
Week 3
(5 hrs)
Machine learning on text data SVM, RF, Ensemble model
Tools: Scikit-learn
Week 4
(5 hrs)
Semi-supervised and unsupervised classification in NLP Topic classification & Named Entity Recognition
Tools : CoRex, LDA
Week 5
(5 hrs)
Deep learning on mix of numeric & text data ANN, LSTM, BERT
Tools: keras
Week 6
(15 hrs)
Case study guidance & evaluation Real-world case study

Register Closed

Course Features

Lectures: Updating
Duration: 40 hours
Students: 40
Certificate: yes
Cost: donation
Skill: beginner
Register Closed

Video

Instructor

Juber Rahman, Ph.D.
Updating...

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