Natural Language Processing Courses

What is Natural Language Processing (NLP)?

This module offers an in-depth introduction to Natural Language Processing (NLP) – the study of computers understanding and generating natural human language. You'll learn the basic concepts and techniques of NLP, as well as applications such as voice and text recognition, text analysis, and machine translation. Furthermore, you’ll discover a variety of tasks executed in NLP, applications of NLP, and how NLP problems are solved. In addition, you'll get an overview of the latest research, tools, and technologies used in NLP today.

Natural Language Processing in AI

The module covers the fundamentals of NLP and its applications in AI, including Sentiment Analysis, Text Classification, and Topic Modeling, among many others. You will learn how to design systems to interpret and process natural language, as well as how to apply the fundamental NLP algorithms and techniques across a variety of tasks. Through real-world examples, you will gain hands-on experience with essential concepts and best practices in the field.

Sentiment Analysis

Discover how NLP techniques can be used to identify and quantify sentiment expressed in text, as well as discover potential insights from these sentiments. Understand how to build an NLP model to detect sentiment in language and explore the latest research and trends in sentiment analysis. Apply the concepts covered to solve real-world problems and gain practical experience with Sentiment Analysis.

Text Analytics

Students will learn to apply various techniques and tools to preprocess and analyze text using Python programming. They will also apply advanced algorithms to analyze the sentiment of text, identify topics within a text, and extract insights from unstructured data.

Preprocessing Text Data

Learn how to implement the steps typically involved in the Text Preprocessing pipeline, such as Tokenization, Stop Words removal, Stemming, and Lemmatization. Additionally, understand how to use regular expressions and Text Cleaning techniques to process text data. Finally, examine the critical differences between performing Preprocessing and using specialized NLP techniques.

Tokenization

This module will introduce you to Tokenization in NLP, including what it is, why it is crucial, and how it can be used to help you get the most out of your NLP system. You'll learn techniques to tokenize the text and explore some popular algorithms used in Tokenization. Additionally, you'll be able to create your own tokenizer and see how it can be used in NLP applications. Finally, you'll get hands-on experience with tokenizing text in Python using NLTK.

Stop Words

This module will provide a comprehensive overview of Stop Words in NLP, an essential concept in text analysis that does not add anything to the sentence’s meaning and can be removed from the sentence without altering its meaning. You will discover how to eliminate Stop Words from the text data set, which will help processing go more quickly and easily.

Stemming and Lemmatization

This module covers the fundamentals of Stemming and Lemmatization, two popular methods in NLP that make text processing easier and faster by reducing the number of unique words in a text. It takes a comprehensive look at how to identify and normalize words in a text, giving you the necessary skills for extracting valuable insights from unstructured data. This module will show you how to use Stemming and Lemmatization algorithms to optimize your NLP analysis, shortening and simplifying words to make them more useful for Data Analysis.

Bag of Words Model

This module introduces you to the Bag of Words Model, an NLP technique of text modeling. You will learn how to create this model from text data and use it for various NLP tasks like Text Classification, Sentiment Analysis, and Topic Modeling. By the end of this module, you'll gain hands-on experience on how to keep track of words, disregard the grammatical details, word order, and other essential techniques.

Web Scraping

This module provides an overview of Web Scraping, an essential component of NLP to extract data from the web. You will learn how to collect and parse web data to obtain relevant information from web pages and other online sources.