Superior Natural Language Processing Course (NLP) with Deep Learning
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Syllabus
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Summary
This natural language processing course (NLP) with Deep Learning will give you a complete vision of how to use natural language processing (PLN) through advanced Deep Learning techniques. It covers from the basic concepts of artificial intelligence, Machine Learning and Deep Learning, to the practical implementation of models in Python. In addition, you will understand the relationship between AI and Big Data, expert systems, clustering and classification algorithms, neural networks and deep learning strategies. You will also know how to carry out the development and application of chatbots using bookstores such as NLTK and tools such as Tensorflow and Keras being able to face real challenges in the AI and PLN industry.
Goals
Professional opportunities
To prepare you
The natural language processing course (NLP) with Deep Learning prepares you to develop and apply artificial intelligence models in various domains, focused on natural language processing. You will learn to use advanced tools such as tensorflow and keras to create neural networks, as well as implement Machine Learning algorithms. You will also be trained to develop chatbots and apply PLN techniques.
Who is it addressed to?
This natural language processing course (NLP) with Deep Learning is designed for students and computer professionals, engineering and related fields who wish to specialize in artificial intelligence and natural language processing. It is also suitable for software developers and data scientists who seek to expand their knowledge.
Official Character
This training is not included in the scope of official regulated training (Infant Education, Primary Education, Secondary Education, Official Professional Training FP, Baccalaureate, University Degree, Official University Master and PhD). It is therefore a complementary and/or specialization training, aimed at acquiring certain skills, skills or aptitudes of a professional nature, being able to be barely as merit in workbags and/or opposition competitions, always within the complementary training section and/or continuous training being always essential to review the specific requirements of the public labor stock of the public labor stock market.
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