Omdena Academy course
Solving Business Problems with NLP
- Skill level
- beginner
- Duration
- 40 hours
- Start date
- February 6, 2022
Who this course is for
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 |