Deep learning has revolutionized natural language processing (NLP) and NLP problems that require a large amount of work in terms of designing new features. Tuning models can now be efficiently solved using NLP.

In this course, you will learn the fundamentals of TensorFlow and Keras, which is a Python-based interface for TensorFlow. Next, you will build embeddings and other vector representations, including the skip-gram model, continuous bag-of-words, and Global Vector representations. You will then learn about convolutional neural networks, recurrent neural networks, and long short-term memory networks. You’ll also learn to solve NLP tasks like named entity recognition, text generation, and machine translation using them. Lastly, you will learn transformer-based architectures and perform question answering (using BERT) and caption generation.

By the end of this course, you will have a solid foundation in NLP and the skills to build TensorFlow-based solutions for a wide range of NLP problems.