Master modern natural language processing techniques by implementing classification and Seq2Seq models with Transformers and TensorFlow 2 in Google Colab.
Natural Language Processing (NLP) has become one of the fastest-growing areas within artificial intelligence. Companies across all sectors use models capable of understanding, classifying, translating, and generating text to develop chatbots, virtual assistants, sentiment analysis systems, machine translation, intelligent search engines, and automation solutions. In this course you'll learn to develop these types of applications using Python, TensorFlow 2, and Google Colab, without installation hassles.
Throughout the course you'll discover how to build modern Natural Language Processing models using neural networks and Transformer architectures, the technology that revolutionized the NLP field and that serves as the foundation for models like ChatGPT, Gemini or Claude. Starting from fundamental concepts, you'll implement solutions capable of analyzing text, classifying documents and translating languages using current deep learning techniques.
Learning is completely practice-oriented. You'll develop real projects like a sentiment analysis system based on Convolutional Neural Networks (CNN) and an automatic translation model using Transformers. Beyond understanding the theory, you'll learn to implement, train, and evaluate models that you can later adapt to your own professional projects.
All the content is developed in Google Colab using TensorFlow 2, so you can start coding from day one without worrying about complex configurations or compatibility issues. If you're looking to build a solid foundation in modern NLP with Python and learn the technologies powering today's AI applications, this course will give you the skills you need to tackle real projects with confidence.
Learn how computers process, represent and interpret human language using modern Natural Language Processing techniques.
Create, clean, and preprocess text datasets to train NLP models efficiently and achieve better results.
Develop a Convolutional Neural Network (CNN) to solve text classification tasks, such as sentiment analysis.
Implement a Transformer from scratch and understand why it revolutionized automatic translation tasks and sequence-to-sequence processing.
Discover how the attention mechanism works and why it's the foundation of today's most advanced language models.
Design custom layers, models, and training processes to develop NLP solutions adapted to any problem.
Learn when to use CNNs, Transformers, and other Deep Learning models based on your Natural Language Processing task.
Implement all projects using Google Colab and TensorFlow 2, with no worries about installations or compatibility issues.
Build complete sentiment analysis and automatic translation projects that you can use as a foundation for real-world applications.
Ligency Team is an international team of experts in programming, artificial intelligence, data science and technology, founded by Kirill Eremenko and Hadelin de Pontevés, creators of some of the world's most popular courses in these disciplines. With millions of students and a multidisciplinary team of over 20 professionals, our mission is to offer practical, high-quality training that helps people at any level develop relevant technological skills. Since 2018, our courses have been available in Spanish thanks to the collaboration with Juan Gabriel Gomila and Frogames Formación, bringing the best educational content to the Spanish-speaking community.
Mathematician, Certified Unity Instructor, and Online Instructor who has trained over 600,000 students worldwide across different platforms such as Udemy and Platzi. CEO of Frogames Formación and driving force behind this platform, bringing all his knowledge in Mathematics, Machine Learning, Videogames, AI and Blockchain among others.
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