Apply Natural Language Processing (NLP) with Python


Summary, Description

Natural Language Processing (NLP) is a very interesting AI-related area and is at the forefront of many helpful chatbot applications. For those seeking a career in AI, experience of NLP is considered a prerequisite. The theory as well as the NLP implementations are provided by this course. Case studies for a deeper understanding of the topic are clarified along with a walkthrough of the codes.

NLP is a computer science and artificial intelligence subfield that is concerned with communications between computers and (natural) human languages. It is used for the text and speech implementation of machine learning algorithms.

We may use NLP, for example, to build programmes such as speech recognition, text description, machine translation, spam identification, recognition of named individuals, answering questions, autocomplete, predictive typing, and so on.

Most of us have smartphones today that have voice recognition. In order to hear what is written, these smartphones use NLP. Many individuals still use laptops whose operating system has built-in understanding of voice.

Several examples:

The 1.Cortana

The Microsoft OS has a virtual assistant that can understand a normal voice, named Cortana. You will use it to set up alerts, open applications, send emails, play sports, track deliveries and flights, check the weather, etc.

2.Siri the 2.Siri

Siri is a personal helper to the operating systems iOS, watchOS, macOS, HomePod and tvOS of Apple Inc. Again, with voice commands, you can do a lot of things: initiate a call, contact someone, send an email, set a timer, take a photo, open an app, set an alarm, use navigation, etc.

3.Gmail announcement

Google’s popular email programme, Gmail, uses spam detection to weed out some spam communications.

We will answer this course with:

Introduction a)NLP:

What does NLP mean?

NLP software

NLP Issues

B)In NLP, key concepts:

Segmentation Of Sentence

Tokenization by Word

Stemming Down

Lemmatization of

Parsing Parsing


NLP Ambiguities

C)The NLP in Motion


Tokenization by Sentence

Tokenization by Word

Stemming Down

Lemmatization of

Removing Noise

From Spacy

Speech Marking Pieces

Parsing of Dependency

Correcting Spell

View Point

Periodic Phrases

Document Flash

Recognition of Designated Object – NER

D)Case Reports Studies:

Recognizance of speech

Through this course, you will not only have excellent technical material, but you will also have access to both our course-related Question and Answer pages, as well as our live student chat page, so that you can partner up with other students for assignments, or get support from myself and the teaching assistants on the course content.

For whom this course is intended:

Computer scientists, Python engineers, ML Experts, data science team management IT administrators
Python developers became involved in studying how to use the encoding of natural languages


Course Link