Apply Natural Language Processing (NLP) with Python

Natural Language Processing (NLP) is a very interesting field associated with AI and is at the forefront of many useful applications like a chatbot. Knowledge of NLP is considered a necessity for those pursuing a career in AI. This course covers both the theory as well as the applications of NLP. Case studies are explained along with a walkthrough of the codes for a better understanding of the subject.

NLP is a subfield of computer science and artificial intelligence concerned with interactions between computers and human (natural) languages. It is used to apply machine learning algorithms to text and speech.

For example, we can use NLP to create systems like speech recognition, document summarization, machine translation, spam detection, named entity recognition, question answering, autocomplete, predictive typing and so on.

Nowadays, most of us have smartphones that have speech recognition. These smartphones use NLP to understand what is said. Also, many people use laptops whose operating system has built-in speech recognition.

Some Examples:

1.Cortana

The Microsoft OS has a virtual assistant called Cortana that can recognize a natural voice. You can use it to set up reminders, open apps, send emails, play games, track flights and packages, check the weather and so on.

2.Siri

Siri is a virtual assistant of the Apple Inc.’s iOS, watchOS, macOS, HomePod, and tvOS operating systems. Again, you can do a lot of things with voice commands: start a call, text someone, send an email, set a timer, take a picture, open an app, set an alarm, use navigation and so on.

3.Gmail

The famous email service Gmail developed by Google is using spam detection to filter out some spam emails.

In this course we will deal with:

a)NLP Introduction:

• What is NLP

• Applications of NLP

• Challenges in NLP

b)Key concepts in NLP:

• Sentence Segmentation

• Word Tokenization

• Stemming

• Lemmatization

• Parsing

• POS

• Ambiguities in NLP

c)NLP in Action

• NLTK

• Sentence Tokenization

• Word Tokenization

• Stemming

• Lemmatization

• Noise Removal

• Spacy

• Parts of Speech Tagging

• Dependency Parsing

• Spell Correction

• Point of View

• Regular Expressions

• Flash Text

• Named Entity Recognition – NER

d)Case studies:

• Speech recognition

• Sentiment analysis

You will not only get fantastic technical content with this course, but you will also get access to both our course-related Question and Answer forums, as well as our live student chat channel, so you can team up with other students for projects, or get help on the course content from myself and the course teaching assistants.

All of this comes with a 30-day money back guarantee, so you can try the course risk-free.

What are you waiting for? Become an expert in natural language processing today!

I will see you inside the course,

Govind Kumar