Solving Business Problems with NLP
Completed Courses
ACF Type: date_picker
ACF Type: text
ACF Type: radio
ACF Type: checkbox
Course Description
For whom is this course
ACF Type: wysiwyg
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
ACF Type: wysiwyg
- 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
ACF Type: wysiwyg
- Basic Python
Syllabus
ACF Type: wysiwyg
| 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 |
Course Features
ACF Type: text
ACF Type: text
ACF Type: text
ACF Type: radio
ACF Type: radio
ACF Type: checkbox
Category: Natural Language Processing
Video
ACF Type: oembed
Upcoming Courses
ACF: none
Array ( [post_type] => Array ( [0] => course )[post_status] => publish [posts_per_page] => 4 [post__not_in] => Array ( [0] => [1] => 33636 )[tax_query] => Array ( [relation] => AND [0] => Array ( [relation] => OR )[1] => Array ( [taxonomy] => status [field] => slug [terms] => Array ( [0] => upcoming-courses )[operator] => IN ))[paged] => 1 [orderby] => date [order] => ASC [meta_query] => Array ( [relation] => AND ))ACF Type: date_picker
ACF Type: date_picker
ACF Type: text
ACF Type: radio
ACF Type: checkbox
ACF Type: date_picker
ACF Type: date_picker
ACF Type: text
ACF Type: text
ACF Type: radio
ACF Type: checkbox
ACF Type: date_picker
ACF Type: date_picker
ACF Type: text
ACF Type: text
ACF Type: radio
ACF Type: checkbox
ACF Type: date_picker
ACF Type: date_picker
ACF Type: text
ACF Type: radio
ACF Type: checkbox
Showing 1-4 of 5 results