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4.33 out of 5
4.33
412 reviews on Udemy

The Ultimate Beginners Guide to Natural Language Processing

Learn step-by-step the main concepts of natural language processing in Python! Build a sentiment classifier!
Instructor:
Jones Granatyr
8,618 students enrolled
English [Auto] More
Understand the basic concepts of natural language processing, such as: part-of-speech, lemmatization, stemming, named entity recognition, and stop words
Understand more advanced concepts, such as: dependency parsing, tokenization, word and sentence similarity
Load texts from the Internet to apply natural language processing techniques
How to visualize the most frequent terms using wordcloud
Implement text summarization and keyword search
Learn how to represent texts using Bag of Words and TF-IDF
Implement sentiment analysis using NLTK library (natural language toolkit), TF-IDF and spaCy library

Natural Language Processing Course:

“Unlock the potential of Natural Language Processing (NLP) with our comprehensive course designed for beginners. Dive into the realm of Artificial Intelligence (AI) where computers are trained to understand and process human language, both written and spoken.

In this course, we focus on leveraging the spaCy and NLTK (Natural Language Toolkit) libraries along with Python programming language to equip you with essential NLP skills. Whether you’re looking to kickstart a new career or enhance your existing one, this course is your gateway to the exciting world of NLP.

Divided into three parts, the course covers a wide range of topics:

  1. Foundational Concepts: Explore basic NLP concepts including part-of-speech tagging, lemmatization, stemming, named entity recognition, stop words, dependency parsing, word and sentence similarity, and tokenization.
  2. Advanced Topics: Delve deeper into NLP with advanced topics such as preprocessing functions, word clouds, text summarization, keyword search, bag of words, TF-IDF (Term Frequency – Inverse Document Frequency), and cosine similarity. Additionally, simulate a chatbot capable of answering questions on any given subject.
  3. Practical Projects: Put your newfound knowledge into practice by creating a sentiment classifier using a real Twitter dataset. Implement the classifier using NLTK, TF-IDF, and the spaCy library.

Whether you’re a complete novice or have some familiarity with NLP, this course caters to all skill levels. By the end of the course, you’ll have the practical background to develop simple NLP projects and progress to more advanced materials.

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Natural Language Processing Course

Includes

6 hours on-demand video
1 article
1 lectures
Certificate of Completion
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