Why this course?

Description

NLP is a branch of artificial intelligence that deals with the interaction between computers and human language. It enables machines to read, understand and get meaning from human language. At the end of the course, the participants will gain unlimited exposure and will effectively learn and deploy Natural Language processing techniques.

Total Duration:15Hrs       Modules:2         Assignments:6        Projects – 2

Module 1:  Introduction to NLP & Basics

1. Introduction to NLP & Basics
2. Pre-processing techniques
3. BOW and TF-IDF model creation
4. Semantic Processing
5. Web Scraping

Module 2: Use cases with projects

6. Information extraction using Spacy
7. OCR
8. Text Summarization
9. Word cloud
10. Recommendation system

Module 1:  Introduction to NLP & Basics

Key Learning Objectives:  This Module will introduce the participants the Overview of NLP and its Application using various techniques like RegEx, Text Encoding etc with LIVE Interactive Sessions and Hands on Programs.

Assignment 1:  Use Reg Ex to find email and phone number in a corpus.

 Lesson 1: Pre-processing techniques

We need to apply various pre-processing steps like Tokenization, stop words removal, Stemming and Lemmatization before you can do any kind of text analytics.

Assignment 2:  Read data from Excel and Text file, create a clean data.

Lesson 2:  BOW and TF-IDF model creation

Create Bag of Words and TF-IDF models.

Assignment 3: Create BOW and TF-IDF model and compare them.

 Lesson 3: Semantic Processing

Learn the techniques and algorithms to understand the meaning of a text in sentence or corpus using Word vector and Word Embeddings.

Assignment 4 -    Find similar words in the corpus.

Lesson 4:  Web Scraping

Using Beautiful soup library, learn how to scrap the content from any website and use it for further analysis.

Project -    Scrap News article, find keywords & Tags and Text analytics.

 

Module 2: Use cases with projects

Key Learning Objectives:

NLP use cases with live project coding classes.

Lesson 5:  NER for information extraction

Use of Named entity recognition to extract information from corpus.

 Lesson 6: OCR

Perform OCR using Tesseract to convert image/corrupted files to readable digital format.

Assignment 5 – Find and showcase on how to read given images, then apply Tesseract OCR technique.

Project -    Read PDF document, find Sentences and similar sentences, Compare for difference.

Lesson 7: Extractive Text Summarization

Apply summarization techniques by generate meaningful summary from sources like news articles, books, blogs etc.

Assignment 6 – Summarize a new article using NLTK or Genism.

Lesson 8: Word Cloud

Word Cloud is the EDA equivalent for NLP. It helps us to understand how different words are distributed across the documents.

 Lesson 9: Recommendation system

Build a Recommendation system using Genism.

 

For Related courses

After completing the course, automatically e-certificate will be generated for the participants.