Udemy course Natural Language Processing for Text Summarization by Jones Granatyr
Natural Language Processing for Text Summarization is the best Udemy course on the market. With this offer they will be able to greatly improve their knowledge and become more competitive within the Development category. Therefore, if you are looking to improve your Development skills we recommend that you download Natural Language Processing for Text Summarization udemy course.
Here you can see Udemy courses linked to: Development.
- Author: Jones Granatyr
- Course rating: 4.5
- Category: Development
- Modality: Online
- Status: Available
- Idiom: English
Abouth Jones Granatyr
Olá! Meu nome é Jones Granatyr e já trabalho em torno de 10 anos com Inteligência Artificial (IA), inclusive fiz o meu mestrado e doutorado nessa área. Atualmente sou professor, pesquisador e fundador do portal IA Expert, um site com conteúdo específico sobre Inteligência Artificial. Desde que iniciei na Udemy criei vários cursos sobre diversos assuntos de IA, como por exemplo: Deep Learning, Machine Learning, Data Science, Redes Neurais Artificiais, Algoritmos Genéticos, Detecção e Reconhecimento Facial, Algoritmos de Busca, Mineração de Textos, Buscas em Textos, Mineração de Regras de Associação, Sistemas Especialistas e Sistemas de Recomendação. Os cursos são abordados em diversas linguagens de programação (Python, R e Java) e com várias ferramentas/tecnologias (tensorflow, keras, pandas, sklearn, opencv, dlib, weka, nltk, por exemplo). Meu principal objetivo é desmistificar a área de IA e ajudar profissionais de TI a entenderem como essa tecnologia pode ser utilizada na prática e que possam visualizar novas oportunidades de negócios.
What the udemy Natural Language Processing for Text Summarization course teaches?
What you’ll learn Understand the theory and mathematical calculations of text summarization algorithms Implement the following summarization algorithms step by step in Python: frequency-based, distance-based and the classic Luhn algorithm Use the following libraries for text summarization: sumy, pysummarization and BERT summarizer Summarize articles extracted from web pages and feeds Use the NLTK and spaCy libraries and Google Colab for your natural language processing implementations Create HTML visualizations for the presentation of the summaries
Understand the basic theory and implement three algorithms step by step in Python! Implementations from scratch!
More information about the course Natural Language Processing for Text Summarization
The area of Natural Language Processing (NLP) is a subarea of Artificial Intelligence that aims to make computers capable of understanding human language, both written and spoken. Some examples of practical applications are: translators between languages, translation from text to speech or speech to text, chatbots, automatic question and answer systems (Q&A), automatic generation of descriptions for images, generation of subtitles in videos, classification of sentiments in sentences, among many others! Another important application is the automatic document summarization, which consists of generating text summaries. Suppose you need to read an article with 50 pages, however, you do not have enough time to read the full text. In that case, you can use a summary algorithm to generate a summary of this article. The size of this summary can be adjusted: you can transform 50 pages into only 20 pages that contain only the most important parts of the text! Based on this, this course presents the theory and mainly the practical implementation of three text summarization algorithms: (i) frequency-based, (ii) distance-based (cosine similarity with Pagerank) and (iii) the famous and classic Luhn algorithm, which was one of the first efforts in this area. During the lectures, we will implement each of these algorithms step by step using modern technologies, such as the Python programming language, the NLTK (Natural Language Toolkit) and spaCy libraries and Google Colab , which will ensure that you will have no problems with installations or configurations of software on your local machine. In addition to implementing the algorithms, you will also learn how to extract news from blogs and the feeds, as well as generate interesting views of the summaries using HTML! After implementing the algorithms from scratch, you have an additional module in which you can use specific libraries to summarize documents, such as: sumy, pysummarization and BERT summarizer. At the end of the course, you will know everything you need to create your own summary algorithms! If you have never heard about text summarization, this course is for you! On the other hand, if you are already experienced, you can use this course to review the concepts.