Become a Natural Language Processing Expert Nanodegree for free

Prerequisite Knowledge

This program requires experience with Python, statistics, machine learning, and deep learning.

• You need to have intermediate to advanced Python experience. You are familiar with object-oriented programming. You can write nested for loops and can read and understand code written by others.
• Intermediate statistics background. You are familiar with probability.
• Intermediate knowledge of machine learning techniques. You can describe backpropagation, and have seen a few examples of neural network architecture (preferrably a recurrent neural network or a long short-term memory network).
• You have seen or worked with a deep learning framework like TensorFlow, Keras, or PyTorch before.

Introduction to Natural Language Processing

Learn text processing fundamentals, including stemming and lemmatization. Explore machine learning methods in sentiment analysis. Build a speech tagging model.

Project – Part of Speech Tagging

Use several techniques, including table lookups, n-grams, and hidden Markov models, to tag parts of speech in sentences, and compare their performance.

Computing with Natural Language

Learn advanced techniques like word embeddings, deep learning attention, and more. Build a machine translation model using recurrent neural network architectures.

Project – Machine Translation

Build a deep neural network that functions as part of an end-to-end machine translation pipeline. Your completed pipeline will accept English text as input and return the French translation. You’ll be able to explore several recurrent neural network architectures and compare their performance.

Communicating with Natural Language

Learn voice user interface techniques that turn speech into text and vice versa. Build a speech recognition model using deep neural networks.

Project – Speech Recognizer

Build a deep neural network that functions as part of an end-to-end automatic speech recognition (ASR) pipeline. The model will convert raw audio into feature representations, which will then turn them into transcribed text.