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This comprehensive specialization covers modern Natural Language Processing (NLP) techniques, combining deep learning models and probability-based approaches. You will start by mastering neural networks, focusing on their role in NLP. Learn to implement text classification using TensorFlow and explore advanced models like convolutional and recurrent neural networks (RNNs). As the specialization progresses, you’ll apply these models to real-world NLP tasks like Named Entity Recognition (NER) and Parts-of-Speech (POS) tagging.
The specialization then dives into NLP using probability models in Python, introducing Markov models and their applications in text classification, article spinning, and cipher decryption. Through hands-on coding exercises, you’ll apply these models to tackle real-world challenges. Ideal for learners with basic programming knowledge who want to master NLP techniques using deep learning and probability models.
By the end of the specialization, you will be able to implement deep learning models for NLP tasks, apply probability models, build NLP applications using Transformers, & solve advanced NLP problems.
Applied Learning Project
The specialization includes hands-on projects that challenge learners to implement text classification models, build advanced neural networks for NLP tasks, and apply Hugging Face Transformers for tasks such as sentiment analysis and text summarization. These projects will help you apply your skills to authentic, real-world problems.
