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Introduction to NLP
course content

Course Content

Introduction to NLP

Introduction to NLP

1. Text Preprocessing Fundamentals
NLP and Its ApplicationsUnderstanding Text PreprocessingTokenizationChallenge: Tokenizing a Sentence Tokenization Using Regular ExpressionsChallenge: Tokenizing Using RegexRemoving Stop WordsApplying Text Preprocessing in Practice
2. Stemming and Lemmatization
StemmingChallenge: Stemming the TokensLemmatizationPart of Speech TaggingLemmatization with POS Tagging
3. Basic Text Models
Overview of Vector Space ModelsKey Types of Vector Space ModelsBag of WordsCustomizing Bag of WordsChallenge: Creating a Bag of WordsTF-IDFImplementing TF-IDF
4. Word Embeddings
Word Embeddings BasicsCBoW and Skip-gram Models (Optional)Implementing Word2VecChallenge: Creating Word Embeddings

bookNLP and Its Applications

In order to feel confident and successfully complete this course, we strongly recommend you complete the following courses beforehand (just click on them to start):

Natural Language Processing (NLP) stands as a pivotal intersection of artificial intelligence (AI), linguistics, and computer science, focusing on the interaction between computers and humans through natural language.

The ultimate goal of NLP is to enable computers to understand, interpret, and generate human languages in a way that is both valuable and meaningful. This technology underpins a variety of applications we use daily, from search engines and voice-activated assistants to language translation services and customer service chatbots.

Applications of NLP

NLP technologies have been seamlessly integrated into numerous aspects of our daily lives, enhancing convenience and efficiency. Some notable applications include the following:

  • Search Engines: Improving the relevance of search results by understanding the intent behind queries;
  • Virtual Assistants: Power voice-activated assistants like Siri, Alexa, and Google Assistant, enabling them to comprehend and respond to voice commands;
  • Language Translation: Enabling real-time translation of languages, breaking down communication barriers across the globe;
  • Sentiment Analysis: Analyzing opinions, sentiments, and emotions from text data, widely used in social media monitoring and brand management;
  • Chatbots and Customer Service: NLP powers chatbots and customer service solutions, including models like ChatGPT, to provide automated, instant responses to customer inquiries, which enhances service efficiency and improves the user experience.

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Section 1. Chapter 1
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