Natural Language processing is today a fast-emerging technology area. It has also been one of the most difficult topics to handle for the computers. Thanks to the advancement in artificial intelligence, we can process natural language more easily today. Many business applications today leverage the power of NLP. With the perfection on speech to text and text to speech conversions, NLP tools are used today as personal assistants and robo advisors. The chat bots are primary interface for many business applications. The NLP engine can process vast amounts of texts and classify them as well as translate them to another language. NLP programs are working in conjunction with the image recognition techniques to automatically generate captions from the images and the vice-versa.

This course is tries to demystify some aspects of NLP and address some of the challenges and approaches to handle the same.

Expectations from the course

1. Why NLP is important

2. Complexity in handling NLP

3. Business use cases of NLP

4. Different types of NLP problems

5. Approach for solving NLP problems

6. Applying machine learning concepts

7. Word embedding

This course uses python programming for basic hands-on. Some of the practice sessions in this course include: -

1. Standard text handling using nltk

2. Pre-processing text (normalization)

3. Sentiment analysis of the review comments

4. Spam detection using machine learning algorithms